{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备工作\n",
    "\n",
    "1.确保您按照[README](README-CN.md)中的说明在环境中设置了API密钥\n",
    "\n",
    "2.安装依赖包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:39:52.300670Z",
     "start_time": "2024-06-11T02:39:50.836749Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install tiktoken openai pandas matplotlib plotly scikit-learn numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 生成 Embedding (基于 text-embedding-ada-002 模型)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "嵌入对于处理自然语言和代码非常有用，因为其他机器学习模型和算法（如聚类或搜索）可以轻松地使用和比较它们。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Embedding](images/embedding-vectors.svg)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 亚马逊美食评论数据集(amazon-fine-food-reviews)\n",
    "\n",
    "Source:[美食评论数据集](https://www.kaggle.com/snap/amazon-fine-food-reviews)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![dataset](images/amazon-fine-food-reviews.png)\n",
    "\n",
    "\n",
    "该数据集包含截至2012年10月用户在亚马逊上留下的共计568,454条美食评论。为了说明目的，我们将使用该数据集的一个子集，其中包括最近1,000条评论。这些评论都是用英语撰写的，并且倾向于积极或消极。每个评论都有一个产品ID、用户ID、评分、标题（摘要）和正文。\n",
    "\n",
    "我们将把评论摘要和正文合并成一个单一的组合文本。模型将对这个组合文本进行编码，并输出一个单一的向量嵌入。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:47:42.236603Z",
     "start_time": "2024-06-11T02:47:41.938167Z"
    }
   },
   "outputs": [],
   "source": [
    "# 导入 pandas 包。Pandas 是一个用于数据处理和分析的 Python 库\n",
    "# 提供了 DataFrame 数据结构，方便进行数据的读取、处理、分析等操作。\n",
    "import pandas as pd\n",
    "# 导入 tiktoken 库。Tiktoken 是 OpenAI 开发的一个库，用于从模型生成的文本中计算 token 数量。\n",
    "import tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 加载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:47:48.727975Z",
     "start_time": "2024-06-11T02:47:48.704968Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B003XPF9BO</td>\n",
       "      <td>A3R7JR3FMEBXQB</td>\n",
       "      <td>5</td>\n",
       "      <td>where does one  start...and stop... with a tre...</td>\n",
       "      <td>Wanted to save some to bring to my Chicago fam...</td>\n",
       "      <td>Title: where does one  start...and stop... wit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1351123200</td>\n",
       "      <td>B003JK537S</td>\n",
       "      <td>A3JBPC3WFUT5ZP</td>\n",
       "      <td>1</td>\n",
       "      <td>Arrived in pieces</td>\n",
       "      <td>Not pleased at all. When I opened the box, mos...</td>\n",
       "      <td>Title: Arrived in pieces; Content: Not pleased...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Time   ProductId          UserId  Score  \\\n",
       "0  1351123200  B003XPF9BO  A3R7JR3FMEBXQB      5   \n",
       "1  1351123200  B003JK537S  A3JBPC3WFUT5ZP      1   \n",
       "\n",
       "                                             Summary  \\\n",
       "0  where does one  start...and stop... with a tre...   \n",
       "1                                  Arrived in pieces   \n",
       "\n",
       "                                                Text  \\\n",
       "0  Wanted to save some to bring to my Chicago fam...   \n",
       "1  Not pleased at all. When I opened the box, mos...   \n",
       "\n",
       "                                            combined  \n",
       "0  Title: where does one  start...and stop... wit...  \n",
       "1  Title: Arrived in pieces; Content: Not pleased...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_datapath = \"data/fine_food_reviews_1k.csv\"\n",
    "df = pd.read_csv(input_datapath, index_col=0)\n",
    "df = df[[\"Time\", \"ProductId\", \"UserId\", \"Score\", \"Summary\", \"Text\"]]\n",
    "df = df.dropna()\n",
    "\n",
    "# 将 \"Summary\" 和 \"Text\" 字段组合成新的字段 \"combined\"\n",
    "df[\"combined\"] = (\n",
    "    \"Title: \" + df.Summary.str.strip() + \"; Content: \" + df.Text.str.strip()\n",
    ")\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:48:01.468539Z",
     "start_time": "2024-06-11T02:48:01.453045Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      Title: where does one  start...and stop... wit...\n",
       "1      Title: Arrived in pieces; Content: Not pleased...\n",
       "2      Title: It isn't blanc mange, but isn't bad . ....\n",
       "3      Title: These also have SALT and it's not sea s...\n",
       "4      Title: Happy with the product; Content: My dog...\n",
       "                             ...                        \n",
       "995    Title: Delicious!; Content: I have ordered the...\n",
       "996    Title: Good Training Treat; Content: My dog wi...\n",
       "997    Title: Jamica Me Crazy Coffee; Content: Wolfga...\n",
       "998    Title: Party Peanuts; Content: Great product f...\n",
       "999    Title: I love Maui Coffee!; Content: My first ...\n",
       "Name: combined, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"combined\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Embedding 模型关键参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:50:51.964826Z",
     "start_time": "2024-06-11T02:50:51.957313Z"
    }
   },
   "outputs": [],
   "source": [
    "# 模型类型\n",
    "# 建议使用官方推荐的第二代嵌入模型：text-embedding-ada-002\n",
    "embedding_model = \"text-embedding-ada-002\"\n",
    "# text-embedding-ada-002 模型对应的分词器（TOKENIZER）\n",
    "embedding_encoding = \"cl100k_base\"\n",
    "# text-embedding-ada-002 模型支持的输入最大 Token 数是8191，向量维度 1536\n",
    "# 在我们的 DEMO 中过滤 Token 超过 8000 的文本\n",
    "max_tokens = 8000  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 将样本减少到最近的1,000个评论，并删除过长的样本\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:50:57.774797Z",
     "start_time": "2024-06-11T02:50:57.447200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置要筛选的评论数量为1000\n",
    "top_n = 1000\n",
    "# 对DataFrame进行排序，基于\"Time\"列，然后选取最后的2000条评论。\n",
    "# 这个假设是，我们认为最近的评论可能更相关，因此我们将对它们进行初始筛选。\n",
    "df = df.sort_values(\"Time\").tail(top_n * 2) \n",
    "# 丢弃\"Time\"列，因为我们在这个分析中不再需要它。\n",
    "df.drop(\"Time\", axis=1, inplace=True)\n",
    "# 从'embedding_encoding'获取编码\n",
    "encoding = tiktoken.get_encoding(embedding_encoding)\n",
    "\n",
    "# 计算每条评论的token数量。我们通过使用encoding.encode方法获取每条评论的token数，然后把结果存储在新的'n_tokens'列中。\n",
    "df[\"n_tokens\"] = df.combined.apply(lambda x: len(encoding.encode(x)))\n",
    "\n",
    "# 如果评论的token数量超过最大允许的token数量，我们将忽略（删除）该评论。\n",
    "# 我们使用.tail方法获取token数量在允许范围内的最后top_n（1000）条评论。\n",
    "df = df[df.n_tokens <= max_tokens].tail(top_n)\n",
    "\n",
    "# 打印出剩余评论的数量。\n",
    "len(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 生成 Embeddings 并保存\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:51:15.477176Z",
     "start_time": "2024-06-11T02:51:15.036357Z"
    }
   },
   "outputs": [],
   "source": [
    "from openai import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:52:26.756265Z",
     "start_time": "2024-06-11T02:52:26.735264Z"
    }
   },
   "outputs": [],
   "source": [
    "# OpenAI Python SDK v1.0 更新后的使用方式\n",
    "client = OpenAI(base_url=\"https://api.chatgptid.net/v1\", api_key=\"sk-GPdSyBBh8MoUVo4b29B46b4581D64fBbA46f7d15156a0c17\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:52:44.422916Z",
     "start_time": "2024-06-11T02:52:43.416342Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# 新版本创建 Embedding 向量的方法\n",
    "# Ref：https://community.openai.com/t/embeddings-api-documentation-needs-to-updated/475663\n",
    "res = client.embeddings.create(input=\"complete\", model=embedding_model)\n",
    "print(res.data[0].embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:53:23.270298Z",
     "start_time": "2024-06-11T02:53:23.251295Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CreateEmbeddingResponse(data=[Embedding(embedding=[-0.010925785638391972, -0.023454206064343452, -0.0013552713207900524, -0.010904882103204727, -0.05373702943325043, 0.02204667590558529, -0.01680675707757473, -0.042030829936265945, -0.013176442123949528, -0.0294606015086174, 0.02338452637195587, 0.02123839035630226, 0.0026391209103167057, -0.019259484484791756, 0.002285496098920703, -0.01413105521351099, 0.04554269090294838, 0.0003540602629072964, 0.009344055317342281, -0.01687643676996231, -0.01680675707757473, 0.006790292449295521, -0.017949504777789116, -0.031188659369945526, -0.004407244734466076, -0.00802014023065567, 0.019398843869566917, -0.02295251190662384, -0.0041773016564548016, 0.01644442230463028, 0.007490573916584253, -0.004476924426853657, -0.012389061041176319, -0.01739206723868847, 0.003720899112522602, -0.027091490104794502, -0.009065336547791958, -0.01413105521351099, -0.0007895587477833033, -0.006239822134375572, 0.012082469649612904, 0.017503555864095688, 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     ]
    }
   ],
   "source": [
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:53:45.781929Z",
     "start_time": "2024-06-11T02:53:45.768610Z"
    }
   },
   "outputs": [],
   "source": [
    "# 使用新方法调用 OpenAI Embedding API\n",
    "def embedding_text(text, model=\"text-embedding-ada-002\"):\n",
    "    res = client.embeddings.create(input=text, model=model)\n",
    "    return res.data[0].embedding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 注意：如果未使用信用卡支付过 OpenAI 账单的同学，可以直接跳过此步骤。\n",
    "\n",
    "### 提醒：非必须步骤，可直接复用项目中的嵌入文件 fine_food_reviews_with_embeddings_1k\n",
    "\n",
    "对于免费试用用户的前48小时，OpenAI 设置了 [速率限制](https://platform.openai.com/docs/guides/rate-limits/overview)\n",
    "\n",
    "如果你已经支付过 OpenAI API 账单，可以尝试取消注释，调用以下代码测试批量 Embedding："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实际生成会耗时几分钟，逐行调用 OpenAI Embedding API\n",
    "\n",
    "# df[\"embedding\"] = df.combined.apply(embedding_text)\n",
    "# output_datapath = \"data/fine_food_reviews_with_embeddings_1k_1126.csv\"\n",
    "# df.to_csv(output_datapath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# e0 = df[\"embedding\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# e0"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.读取 fine_food_reviews_with_embeddings_1k 嵌入文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:54:31.713797Z",
     "start_time": "2024-06-11T02:54:31.493797Z"
    }
   },
   "outputs": [],
   "source": [
    "embedding_datapath = \"data/fine_food_reviews_with_embeddings_1k.csv\"\n",
    "\n",
    "df_embedded = pd.read_csv(embedding_datapath, index_col=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 查看 Embedding 结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:54:35.849497Z",
     "start_time": "2024-06-11T02:54:35.828497Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12     [-0.0005399271612986922, -0.004124758299440145...\n",
       "13     [0.0068963742814958096, 0.0167608093470335, -0...\n",
       "14     [-0.0023715533316135406, -0.021357767283916473...\n",
       "15     [0.00226533692330122, 0.010306870564818382, 0....\n",
       "16     [-0.027459919452667236, -0.009041198529303074,...\n",
       "                             ...                        \n",
       "447    [0.00796585250645876, 0.0017102764686569571, 0...\n",
       "436    [0.001777207711711526, -0.011673098430037498, ...\n",
       "437    [-0.005498920567333698, -0.014834611676633358,...\n",
       "438    [-0.00294404081068933, -0.007058987859636545, ...\n",
       "439    [-0.006043732166290283, -0.000693734094966203,...\n",
       "Name: embedding, Length: 1000, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded[\"embedding\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:54:40.718067Z",
     "start_time": "2024-06-11T02:54:40.700631Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34410"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_embedded[\"embedding\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T02:54:49.724809Z",
     "start_time": "2024-06-11T02:54:49.704810Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_embedded[\"embedding\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-23T15:14:11.189894Z",
     "start_time": "2024-05-23T15:14:11.172893Z"
    },
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded[\"embedding\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:01:27.276223Z",
     "start_time": "2024-06-11T03:01:24.156548Z"
    }
   },
   "outputs": [],
   "source": [
    "import ast\n",
    "\n",
    "# 将字符串转换为向量\n",
    "df_embedded[\"embedding_vec\"] = df_embedded[\"embedding\"].apply(ast.literal_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:01:27.291339Z",
     "start_time": "2024-06-11T03:01:27.277215Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1536"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_embedded[\"embedding_vec\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-05-23T15:14:17.381021Z",
     "start_time": "2024-05-23T15:14:17.372022Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "      <th>n_tokens</th>\n",
       "      <th>embedding</th>\n",
       "      <th>embedding_vec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>B000K8T3OQ</td>\n",
       "      <td>AK43Y4WT6FFR3</td>\n",
       "      <td>1</td>\n",
       "      <td>Broken in a million pieces</td>\n",
       "      <td>Chips were broken into small pieces. Problem i...</td>\n",
       "      <td>Title: Broken in a million pieces; Content: Ch...</td>\n",
       "      <td>120</td>\n",
       "      <td>[-0.0005399271612986922, -0.004124758299440145...</td>\n",
       "      <td>[-0.0005399271612986922, -0.004124758299440145...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>B0051C0J6M</td>\n",
       "      <td>AWFA8N9IXELVH</td>\n",
       "      <td>1</td>\n",
       "      <td>Deceptive description</td>\n",
       "      <td>On Oct 9 I ordered from a different vendor the...</td>\n",
       "      <td>Title: Deceptive description; Content: On Oct ...</td>\n",
       "      <td>157</td>\n",
       "      <td>[0.0068963742814958096, 0.0167608093470335, -0...</td>\n",
       "      <td>[0.0068963742814958096, 0.0167608093470335, -0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ProductId         UserId  Score                     Summary  \\\n",
       "12  B000K8T3OQ  AK43Y4WT6FFR3      1  Broken in a million pieces   \n",
       "13  B0051C0J6M  AWFA8N9IXELVH      1       Deceptive description   \n",
       "\n",
       "                                                 Text  \\\n",
       "12  Chips were broken into small pieces. Problem i...   \n",
       "13  On Oct 9 I ordered from a different vendor the...   \n",
       "\n",
       "                                             combined  n_tokens  \\\n",
       "12  Title: Broken in a million pieces; Content: Ch...       120   \n",
       "13  Title: Deceptive description; Content: On Oct ...       157   \n",
       "\n",
       "                                            embedding  \\\n",
       "12  [-0.0005399271612986922, -0.004124758299440145...   \n",
       "13  [0.0068963742814958096, 0.0167608093470335, -0...   \n",
       "\n",
       "                                        embedding_vec  \n",
       "12  [-0.0005399271612986922, -0.004124758299440145...  \n",
       "13  [0.0068963742814958096, 0.0167608093470335, -0...  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 使用 t-SNE 可视化 1536 维 Embedding 美食评论"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:01:50.062227Z",
     "start_time": "2024-06-11T03:01:49.069849Z"
    }
   },
   "outputs": [],
   "source": [
    "# 导入 NumPy 包，NumPy 是 Python 的一个开源数值计算扩展。这种工具可用来存储和处理大型矩阵，\n",
    "# 比 Python 自身的嵌套列表（nested list structure)结构要高效的多。\n",
    "import numpy as np\n",
    "# 从 matplotlib 包中导入 pyplot 子库，并将其别名设置为 plt。\n",
    "# matplotlib 是一个 Python 的 2D 绘图库，pyplot 是其子库，提供了一种类似 MATLAB 的绘图框架。\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "# 从 sklearn.manifold 模块中导入 TSNE 类。\n",
    "# TSNE (t-Distributed Stochastic Neighbor Embedding) 是一种用于数据可视化的降维方法，尤其擅长处理高维数据的可视化。\n",
    "# 它可以将高维度的数据映射到 2D 或 3D 的空间中，以便我们可以直观地观察和理解数据的结构。\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:01:56.526725Z",
     "start_time": "2024-06-11T03:01:56.516725Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_embedded[\"embedding_vec\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:02:42.780853Z",
     "start_time": "2024-06-11T03:02:42.774853Z"
    }
   },
   "outputs": [],
   "source": [
    "# 首先，确保你的嵌入向量都是等长的\n",
    "assert df_embedded['embedding_vec'].apply(len).nunique() == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:02:45.161063Z",
     "start_time": "2024-06-11T03:02:45.106056Z"
    }
   },
   "outputs": [],
   "source": [
    "# 将嵌入向量列表转换为二维 numpy 数组\n",
    "matrix = np.vstack(df_embedded['embedding_vec'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:02:47.775279Z",
     "start_time": "2024-06-11T03:02:47.761286Z"
    }
   },
   "outputs": [],
   "source": [
    "# 创建一个 t-SNE 模型，t-SNE 是一种非线性降维方法，常用于高维数据的可视化。\n",
    "# n_components 表示降维后的维度（在这里是2D）\n",
    "# perplexity 可以被理解为近邻的数量\n",
    "# random_state 是随机数生成器的种子\n",
    "# init 设置初始化方式\n",
    "# learning_rate 是学习率。\n",
    "tsne = TSNE(n_components=2, perplexity=15, random_state=42, init='random', learning_rate=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:03:16.690761Z",
     "start_time": "2024-06-11T03:03:13.988383Z"
    }
   },
   "outputs": [],
   "source": [
    "# 使用 t-SNE 对数据进行降维，得到每个数据点在新的2D空间中的坐标\n",
    "vis_dims = tsne.fit_transform(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:03:16.706865Z",
     "start_time": "2024-06-11T03:03:16.691754Z"
    }
   },
   "outputs": [],
   "source": [
    "# 定义了五种不同的颜色，用于在可视化中表示不同的等级\n",
    "colors = [\"red\", \"darkorange\", \"gold\", \"turquoise\", \"darkgreen\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:03:20.526119Z",
     "start_time": "2024-06-11T03:03:20.511119Z"
    }
   },
   "outputs": [],
   "source": [
    "# 从降维后的坐标中分别获取所有数据点的横坐标和纵坐标\n",
    "x = [x for x,y in vis_dims]\n",
    "y = [y for x,y in vis_dims]\n",
    "\n",
    "# 根据数据点的评分（减1是因为评分是从1开始的，而颜色索引是从0开始的）获取对应的颜色索引\n",
    "color_indices = df_embedded.Score.values - 1\n",
    "\n",
    "# 确保你的数据点和颜色索引的数量匹配\n",
    "assert len(vis_dims) == len(df_embedded.Score.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:03:22.656038Z",
     "start_time": "2024-06-11T03:03:22.520043Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Amazon ratings visualized in language using t-SNE')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SCSorK4lEIlN+0uk0Y2NjAOXX2Ny5c6fsNxKJEAwGX3G7YPr1P7C/2b5eD3b//fezevVqHA4HoVCoPBw507l7LV93xyKXy/GlL32JhoYG7HY74XCYSCRCPB6f0s7e3t5ZPXd7ezsAF1xwwbRr+Ne//rV8DV+tc889l2uuuYbbbruNcDjMO9/5Tu68885peXKHc7TrfODv3KF/f17tjMx0Os3IyEj55+AkWVmW+fjHP86mTZuYmJjgT3/6E29729t4/PHHjzjc++lPf5pAIHDUXBW3281FF1007UdMTxYO6/HHH2d4eJh77rmHe+65Z9rtd999N5dccsmUbYebRXG47YZhABCPxzn33HPx+Xx85Stfoa2tDYfDwebNm/mnf/qnaR+c69ev59Of/jQf/vCH+chHPjLjvmdbLO1obTuamXoPfv3rX3PzzTdz1VVX8Y//+I9UVlaiKAq33347nZ2ds9rv8WrvwZxOJ+vWreOJJ57ggQce4KGHHuJ//ud/uOCCC/jrX/96xFkx119/PV/4whf43e9+xy233MJvf/tb/H4/l1122RGf87rrruPss8/mD3/4A3/961/5j//4D77xjW9w3333lb95H+7aaZo2pU3H4zz/8Y9/5Bvf+AZf/epXpx2LrutUVlbOmFAOvKpcjNl6ra7/008/zZVXXsk555zDHXfcQU1NDVarlTvvvJPf/OY3x+154cjX91Cf/OQnufPOO7nllltYs2YNfr8fSZK44YYbjqkX8IADj/nVr341Jb/qgKPNtJtt2yVJ4t577+WFF17gL3/5Cw8//DAf/OAH+da3vsULL7wwrY7IoV7L830svvnNb5ZLJoCZmzJT4nJFRQVXXnklV155Jeeddx5PPfUUvb295VyWgx3oVbn11luP2qvyZiQClePo7rvvprKykh/84AfTbrvvvvv4wx/+wI9+9KMjdvPP1pNPPsnk5CT33Xcf55xzTnl7d3f3tPuOj49z7bXXsmLFihnb1tTUhK7rtLe3s3DhwvL20dFR4vH4jG+k19q9995La2sr991335Q/bIdO6z4elWcrKytxOBx0dHRMu22mbbIsc+GFF3LhhRfy7W9/m69//ev8y7/8C0888QQXXXTRYZ+npaWFM844g//5n//hE5/4BPfddx9XXXXVrOqS1NTU8LGPfYyPfexjjI2Nceqpp/J//+//LQcqwWCQeDw+7XG9vb20traWf5/teZ6tffv2cdNNN3HVVVfNOPOpra2NRx99lLVr1x7xdX/gNdbe3j6lvePj46+o5+N4+P3vf4/D4eDhhx+ecs3uvPPOV7S/Y3ndHegdOPQaH9rbCeY1vummm/jWt75V3pbP56c9tqmpaVbPfaCHrLKy8oiv78M5uO0HhncO13aA1atXs3r1av7v//2//OY3v+E973kP99xzDx/+8IeP+bkPduDvXHd395Seu5nOwUwO97fn/e9/P2eddVb599n8fT/ttNN46qmnGB4ePuzf11tuuYXvfOc73HbbbVPO21uBGPo5TnK5HPfddx9XXHEF11577bSfT3ziE6RSKf785z+/Js934NvDwd8WisUid9xxx5T7aZrGDTfcQLFY5Pe///2M9Ure/va3A+bc/oN9+9vfBphVVvyrNdPxvPjiizz//PNT7ncgc36mD+VX89wXXXQRf/zjH6fkgXR0dPDggw9Oue+hU8SB8hj/bLqor7/+el544QX++7//m4mJiaMO+2iaNm1YobKyktra2inP19bWxgsvvDBlhsz9999Pf3//lMfO9jzPRjqd5uqrr6auro677rprxj/k1113HZqm8dWvfnXabaqqlq/jRRddhNVq5Xvf+96Uth36mjyRFEVBkqQpPQE9PT388Y9/fMX7m+3rzufzEQ6HWbdu3ZTth77fD+z30F6E733ve9N6MC699FKef/75KZVWo9HotN6vSy+9FJ/Px9e//vUZc0dmqgdysAOBzsFtz2Qy3HXXXVPuF4vFprX7WN5bR3PppZcC08/Z9773vVk9/kDe1aF/e1pbW6cMuxyYjjwyMsKuXbum7adYLPLYY4/NONx+sAO9Kn/605+OqRrum4HoUTlO/vznP5NKpbjyyitnvH316tVEIhHuvvvuo344zcaZZ55JMBjkpptu4lOf+hSSJPGrX/1q2hv9Rz/6EY8//jh/93d/Ny3praqqiosvvpjly5dz00038ZOf/KQ8pLR+/Xruuusurrrqqhnrf7zWrrjiCu677z6uvvpqLr/8crq7u/nRj37EokWLSKfT5fs5nU4WLVrE//zP/zBv3jxCoRBLlixhyZIlr+r5b731Vv7617+ydu1a/v7v/x5N0/j+97/PkiVLpvyR+MpXvsK6deu4/PLLaWpqYmxsjDvuuIP6+vop36oO57rrruNzn/scn/vc5wiFQkf9hppKpaivr+faa69l+fLleDweHn30UTZs2DDlG/OHP/xh7r33Xi677DKuu+46Ojs7+fWvfz0lXwRmf55n47bbbmPXrl3867/+K3/605+m3NbW1saaNWs499xz+ehHP8rtt9/OSy+9xCWXXILVaqW9vZ3f/e53/Nd//RfXXnstkUiEz33uc9x+++1cccUVvP3tb2fLli08+OCDhMPhY2rX8XL55Zfz7W9/m8suu4y/+Zu/YWxsjB/84AfMmTOHbdu2vaJ9zvZ1B+Y1/rd/+zc+/OEPc9ppp7Fu3Tr27ds3bZ9XXHEFv/rVr/D7/SxatIjnn3+eRx99tDwl+oDPf/7z/PrXv+biiy/mk5/8ZHl6cmNjI9FotBx4+nw+fvjDH/K+972PU089lRtuuIFIJEJfXx8PPPAAa9eu5fvf//5hj/GSSy6hsbGRD33oQ/zjP/4jiqLw3//93+V9HHDXXXdxxx13cPXVV9PW1kYqleKnP/0pPp+v/GXq1Vi5ciXXXHMN3/nOd5icnCxPTz5wDo/WW9vW1kYgEOBHP/oRXq8Xt9vNqlWrDptzNzAwwBlnnMEFF1zAhRdeSHV1NWNjY/y///f/2Lp1K7fccstRX9uf/vSn+c///E+2bt0643TjRCLBr3/96xkf+4YuBHdC5hq9BbzjHe8wHA6HkclkDnufm2++2bBarcbExER5uuF//Md/TLnPwdPUDnZgKt/BU/eeffZZY/Xq1YbT6TRqa2uNz3/+88bDDz9sAMYTTzxhGMbLU/dm+jl42mqpVDJuu+02o6WlxbBarUZDQ4PxhS98Ydo056amJuPyyy+fdmyHToOdyeGO2TDMadxf//rXjaamJsNutxunnHKKcf/99884RfS5554zVq5cadhstinTCg83PXmmqb0zTed97LHHjFNOOcWw2WxGW1ub8bOf/cz47Gc/azgcjin3eec732nU1tYaNpvNqK2tNW688UZj3759Rzz2g61du3bG6eAHt/nAMRUKBeMf//EfjeXLlxter9dwu93G8uXLjTvuuGPa4771rW8ZdXV1ht1uN9auXWts3Lhx2nU5lvN8cDsMY/p00ptuuumwr61Dz+1PfvITY+XKlYbT6TS8Xq+xdOlS4/Of/7wxNDRUvo+macZtt91m1NTUGE6n0zjvvPOMHTt2HHbq9ZHOm2G8/Ho4dCr50aZZHzDTOfn5z39uzJ0717Db7caCBQuMO++883V53RmGOe34Qx/6kOH3+w2v12tcd911xtjY2LTjjsVixgc+8AEjHA4bHo/HuPTSS409e/bM+Nxbtmwxzj77bMNutxv19fXG7bffbnz3u981AGNkZGTKfZ944gnj0ksvNfx+v+FwOIy2tjbj5ptvNjZu3HjE82gY5tTgVatWGTabzWhsbDS+/e1vT7sOmzdvNm688UajsbHRsNvtRmVlpXHFFVdM2/+ruc6ZTMb4+Mc/boRCIcPj8RhXXXVVuXTAv/3bvx31OP70pz8ZixYtKk8fP9JU5WQyafzXf/2Xcemllxr19fWG1Wo1vF6vsWbNGuOnP/3plGn4h/u7f/DxHcv05Df6R71kGMc5s0gQ3kSuuuoqdu7cWZ75IAivhxP5urvlllv48Y9/TDqdPqmXTHitvPTSS5xyyin8+te/njY1WzgxRI6KIBxGLpeb8nt7ezv/+7//O23xQ0F4LZ3I192hzz05OcmvfvUrzjrrrDdlkHLo8YKZByXL8pRJCcKJJXJUBOEwWltbufnmm2ltbaW3t5cf/vCH2Gw2Pv/5z5/opglvYifydbdmzRrOO+88Fi5cyOjoKD//+c9JJpP8n//zf477c58I//7v/86mTZs4//zzsVgsPPjggzz44IN85CMfmVa7SjhxxNCPIBzGBz7wAZ544glGRkaw2+2sWbOGr3/968e8no4gHIsT+br74he/yL333svAwACSJHHqqafy5S9/+RVNQ34jeOSRR8pJ4Ol0msbGRt73vvfxL//yL2+KldffLESgIgiCIAjCSUvkqAiCIAiCcNISgYogCIIgCCetN/wgnK7rDA0N4fV6j0s5dUEQBEEQXnuGYZBKpaitrUWWD99v8oYPVIaGhkR2tiAIgiC8QfX391NfX3/Y29/wgYrX6wXMA/X5fCe4NYIgCIIgzEYymaShoaH8OX44b/hA5eD1J0SgIgiCIAhvLEdL2xDJtIIgCIIgnLREoCIIgiAIwklLBCqCIAiCIJy0RKAiCIIgCMJJ67gHKoODg7z3ve+loqICp9PJ0qVL2bhxY/l2wzD40pe+RE1NDU6nk4suuuiELGUuCIIgCMLJ57gGKrFYjLVr12K1WnnwwQfZtWsX3/rWtwgGg+X7/Pu//zvf/e53+dGPfsSLL76I2+3m0ksvJZ/PH8+mCYIgCILwBnBcFyX853/+Z5599lmefvrpGW83DIPa2lo++9nP8rnPfQ6ARCJBVVUVv/jFL7jhhhuO+hzJZBK/308ikRDTkwVBEAThDWK2n9/HtUflz3/+M6eddhrvfve7qays5JRTTuGnP/1p+fbu7m5GRkamLCHu9/tZtWoVzz///Iz7LBQKJJPJKT+CIAiCILw5HddApaurix/+8IfMnTuXhx9+mL//+7/nU5/6FHfddRcAIyMjAFRVVU15XFVVVfm2Q91+++34/f7yjyifL7yhJBIwOgpdXdDfD5OTcPw6NQVBEN7wjmtlWl3XOe200/j6178OwCmnnMKOHTv40Y9+xE033fSK9vmFL3yBz3zmM+XfD5TgFYST2vAwbNsGmzaZQUouB4EAtLXBwoWwciVUVp7oVgqCIJx0jmugUlNTw6JFi6ZsW7hwIb///e8BqK6uBmB0dJSampryfUZHR1mxYsWM+7Tb7djt9uPTYEE4Hvr74d57Yft2szdFlsFmg717zaBl61bztve9Dw56HwiCIAjHeehn7dq17N27d8q2ffv20dTUBEBLSwvV1dU89thj5duTySQvvvgia9asOZ5NE4TXR7EIv/wlPPOMGZQcGPbZsgXGx2FsDCYm4Omn4Re/ADHbTRAEYYrjGqj8wz/8Ay+88AJf//rX6ejo4De/+Q0/+clP+PjHPw6YCxHdcsstfO1rX+PPf/4z27dv5/3vfz+1tbVcddVVx7NpgvD6ePJJ2LDB7EFJpyEWg6Eh8/8DA2aOyvAweDywcaMZsAiCIAhlx3Xo5/TTT+cPf/gDX/jCF/jKV75CS0sL3/nOd3jPe95Tvs/nP/95MpkMH/nIR4jH45x11lk89NBDOByO49k0QTj+CgVzSMcwzIBkYgIymZeHfopF83eLBUZGwOuFXbvgzDPB7T7RrRcEQTgpHNc6Kq8HUUdFOGkNDsJPfgLr10Myaf6eSIDLZQYrhmEGKoEA1NeDwwHnnQc33WT+LgiC8CZ2UtRREYS3NMMwe05U1QxMDpCk6ffTdbNnpVicfrsgCMJbmAhUBOF4CQTMIRyXCxTFDEQsFnNqsqaZ/7dYzMDE5TKDGq8XwuET3XJBEISThghUBOF48Xhg0SIzGKmtBZ/PDEgMwwxSDMPcFgpBJGI+ZulSENPvBUEQyo5rMq0gvOWdfbY56+f5580aKZIEfj+kUmbQ4nCYvSiFApx+Opx//olusSAIwklFBCqCcDw5nXDzzWavys6dZr6KpsHcuWZwomlmALN4MVx7rTn8IwiCIJSJQEUQjre6OjNY2bAB9uwxC77FYmbPSmurWT5/2TLzfoIgCMIUIlARhNdDVRVcfjmsWmXmpxSL5tCP02km3b6FZvokc0mG4kOUtBJuu5v6YD02i+hJEgRhZiJQEYTXiyS9nDT7JqYbBildQzUMHLKMW1YA0HSNTT2b2DawjXgujiRJSJJEta+aM9vOpDncfGIbLgjCSUkEKoIgvGZG1SIdxRzjWgnNMLBi4MhnCKt5ekf2sGd4DzWBGuZWzUWWZEpaicHYII/uepTLl11OTUAsyigIwlQiUBEE4TUxVCqwMZ+iqOuEFCvpfIIdo/sYyqeRMlGG9zyK1+pEURRKWglVV7EpNuqD9XRPdLNzaKcIVARBmEYEKoIgvGqqYbC7kEUzDGqtdlL5NNv6XyKVT9LoCdNXdGI4fKilHA9sewC/00/YG0aWZALOAFW+Knome8gWsrjsrhN9OIIgnEREoCIIwqs2qZWIaSUiFisAA7F+4rk4DR4nueQOipOD9Ee7cGk6kiShaiphdxhFUYhlYvT29FIsFdkzvIeQO8SatjWc2XYmdqsoficIb3UiUBEE4VUrGgY6YJVkVE1jLNFNtbqNkY7dJDNxsvkC8clBUoaNKn8zOTVHKp8i7A0zGBvk+a7n0XSN9tF2kOD3m3/PmtY1fOkdX6LSV3miD08QhBNIBCqCILxqVklCwhwCMvQC/vyLdA2vJ6O68PubwZnHNRJFVnOkMv0YShDN0Ng3uo+NPRvRdZ1KbyXzqudhVaxEM1Ee3/s4VsXKt677FrIsVvsQhLcq8e4XBOFVCytWgoqVqFbCxSilVCfDGZmgrxJFlikh43d58Ti8QI50fpJkLslLfS+RU3NU+atw2Bzoho4sy4S9YSLuCM93Pc9L/S+d6MMTBOEEEoGKIAivmkWSWGB3gSSRzncSy2lYrQ40DPKSjEuRCVntVLkDVLrs2GSDTCFjDv94wnjsHqyyFYvycidvxBchlU+xuXfzCTwyQRBONDH0IwjCa6LeakcGhrMFDMWF0+UmVczjkiRCEsRsFvrjk9R4XSgWBzaLDQMDyZDIFXNUeCqwKtby/mRJxjAMSnrpxB2UIAgnnAhUBEF4RVRVZUv/FnYM7iBXylHlq2JVyypWeBrp9nvYF4eA3UYiPU6hVKTG5yOvqoynR0mWNKrsKrIkM5oaJeKNTJvhk86nsVqstIXbTswBCoJwUhCBiiAIxyyejfODx3/A5t7NFLQCFsVCSS3xl61/4X2nXciKmmYGEx347RXUeiPkdZ2o7CAVGiOdSOAvOaiw+ZmTTbB7aBeyJBPPxnHb3NgsNkpaiZ7JHpbULeG8Beed6MMVBOEEEoGKIAjH7GdP/4znOp9jTtUcfA4fALqu0zvZy0+fv58vnn8Rp9VG2TDYy5ASIBOaw4imETWsVFUuJhBcjNNiZ0HratzP3cXOwe30R/spqAVsso1UIUVLuIXPXfI5sWChILzFiWRaQRCOyb6RfWzq3URTRVM5SAGQZZmWSAv5UoEH2sdYs+ijvGPp+Tir20hbXNhzKSptIeaFFtNoc+GTLWQ9EVYuvpSGUAOarjEcG0aSJP5m9d/w3Ru/yxmtZ5zAIxUE4WQgelQEQTgm+8b2kc6nmVM5Z8bbK72V7B1tJ2nUEWz4MPWhUeYj0T7cQV9sEIfVLJFvQ2dD/w7aezcSjfahyEp5HaDRxCh+p//1PCxBEE5SokdFEIRjoutmGfzDkRUZXddRNZW0YVDAgdvqo8ITQdNVdEMDYMfAdnb3bUZ2+GgIt1HprWRR9SIqPBU8tfcpvvHQN9B1/fU6LEEQTlIiUBEE4ZjUB+uxWWyk8qkZb59ITVAfqifkDpW3GYZBpbeSkDvEaGKUklpk7+g+QMJjd5MrZXHYHHgdXio8FQRdQV7sepGdQztfp6MSBOFkJQIVQRCOybK6ZSysXkjnWCeqpk65bTI9iaqrnDvvXGRZJiBbcMoyWUPHbrWzrH45AXeAvaP7mEiPY3P5ySaGsUsSVd6qcsG3sDdMPBdnW/+2E3GIgiCcRESOiiAIx8RisfDBsz7Idx/7LtsGtplVZS1WUvkUFtnCZUsu48IFFwLgUyw0WBzsLWawIBF0BVjTugabYmfj8G4cVjs+Q8UTqJtSlVaRFTBA2z9MJAjCW5cIVARBOGYtkRb+5fJ/4en2p1nfvZ5sMcuKhhWsnbOW05tOx2J5+U/LYoeLEjr9pQKqZqBIEKyaR8RfRaJ/KwGbA5Spf4ri2TgOq4P5VfNf70MTBOEkIwIVQRBekbA3zNWnXs3Vp159xPvZJJmVDi/NVgfjaokSBm67hyFnkHsmOon5awm6g+X7F0oFBmODrGlbw2nNpx3vwxAE4SQnAhVBEI47RZKotNioPKh428fOvImh8Xae7XiWkeQILpuLQqlAXs2zoHYB//S2f0JRlBPYakEQTgYiUBEE4YQIeUP827v+jb9s/QuP7nmU0cQobrubc+edyzUrr6EmUHOimygIwklAMgzDONGNeDWSySR+v59EIoHP5zv6AwRBOOlouoaqqVgVK7IsJiMKwlvBbD+/RY+KIAgnnCIr5kwfQRCEQ4ivLoIgCIIgnLREoCIIgiAIwklLBCqCIAiCIJy0RKAiCIIgCMJJSyTTCoLwllUoFTAwsFvsR1wRWhCEE0cEKoIgvOUMxgbZPbybvmgfhVIBj8PDvKp5nNp4KlaL9UQ3TxCEg4hARRCEN63R5Chd410MJ4aRJZnWSCvRTJSHdzzMYHyQfDFPQSug6+bqzqc0nsK1K69lXtU80cMiCCcJEagIgvCmtHNwJ890PEMqn8Jj96AZGuv2rWNT7yacVieqrjKWHEMzNHxOH2F3mPXd6ylpJd516rtYVr/sRB+CIAiIQEV4kyuqRXRdx2FznOimCK+j4fgw69rXYVWszK82V2DOFrM81/Eco8lRbIqNRC5BSS+BAbFMjLHkGGFPmI7RDl7sfJHmimZ8TlHtWhBONBGoCG9KG3s28sTeJ9g7shcMmFc9j3Pnncuq1lUnumnC66B9rJ1sMcu8qnnlbaPJUUaSI0iSxFBiCAmJgCuAIisUVHMxxHwxz0B8gH1j++iP9rO4bvEJPApBEEAEKsKb0J9f+jO/efE3ZEtZKtwVSJLEsx3Psrl3M9effj1Xn3r1iW6icJwNxAbwOab2hkTTUQqlAsWS2ctms9jKibNOmxM1p5LMJ6nyVzGUGCKdT5+IpguCcAgRqAhvKu2j7fxu4++wKlaWVy2noBZQNZUKdwVjyTHu3XwvC2oWsLBm4XFvS6FUYCw1hqabORAhd+i4P+froagW6Y/2M5YawzAMwp4wjRWNOKwn1/CawdT1Vkt6iZJawmqxohv6tMUPFVmhqBUJOANk8hlyau71bK4gCIchAhXhTeXFrheJZqMsrF5Iz0QP0WwUTdNQZAWf08d4apwXOl84roGKruvsGNzBtoFtjKfH0XUdt91Na6SVVS2r8Lv8x+25j6dkLslL/S/x5N4nmUhP4Hf6sSk2MsUMVd4qLl58MYtrF58Us2Vaw6083f40Nf6a8ja/0w8yuGwus40GGIaBJElomkZBK+CxeVAUBZtso9pXfQKPQBCEA0SgIryp9Ez0YJEtdIx3kM6ncdld5RkeE6kJErkE2wa2Hdc2bOnbwrr2dbhsLpormlFkhVQ+xdaBrSTzSS5bfBkeh+e4tuG1tmd4D0/ue5Jn2p8hkUtgUSxs7duKJElEvBF2De1i28A2rjrlKi5YeAFeh/eEtndO5Rx2De2ib7KP+lA9siRT46/Ba/cyWhgl5ApR0kvEs3EUScEwDFx2F36Xn0whw/KG5TSEGk7oMQiCYHrdSuj/27/9G5Ikccstt5S35fN5Pv7xj1NRUYHH4+Gaa65hdHT09WqS8CbksDmYSE+QyqcIuUM4rU4UWcFusRPyhMiX8kykJ9B1/bg8fzKXZEvfFvxOP7WBWiyKBUmS8Dl9zK2cS/d4Nx1jHcfluY+XwdggT+x9gsHoIPFMnFwxx76RffTGeumZ7KFjvAObxUamkOHp9qd5aMdDbB/YzsM7HuZPL/2Jp9ufZig+hGEYR3+y10jYG+aChRfgdrjZN7KPzvFOxlPjNAQbCLgCtEZaCbgCBF1BbFYbHqcHv8NPxBOhJdzCpUsuxW13v27tFQTh8F6XHpUNGzbw4x//mGXLptYl+Id/+AceeOABfve73+H3+/nEJz7Bu971Lp599tnXo1nCm9C8ynmk82n8Tv+0IQhVU7EpNjwOD2OpMar9r6BrP5eDgQHz/3Y71NeD++UPtIHYAIlcgrlVc6c99MDw067hXSyrXzYtR2ImhmFQwkACrNKJWZpr9/BueiZ62DO8h90ju9EMjWwhiyzJSEiMJEZI5pMEnAE8dg8PbHuAjb0bqQvUYVWsdIx1sG1gG6c3nc7pLae/bkNDzeFmrvVcS89kD5PpSWRZ5rz557GpdxM7h3YSiAVI5BNYZTNnxWaxsbBmIWfPPZuVjStflzYKgnB0xz1QSafTvOc97+GnP/0pX/va18rbE4kEP//5z/nNb37DBRdcAMCdd97JwoULeeGFF1i9evXxbprwJrSkbgkRb4TRxChW2Vqun5Iv5hlODNNY0UhdoI5c8RUkSu7ZAy++iDY6SjwXp1AqoAf9ONecTWj1uUiyTEEtgMRhP4wdVgeFUgFN144YqOiGwZBapLeUJ6FmsesJKi12auyVhK2v3zf9olpkU+8mhuJDxHNxM0FVh1wxh2ZoyJKMTbHhs/uIZqKs71lPxBuhPlhPS7ilvJ9YJsZzXc8R8oSYUznndWu/x+FhSd2SKduaw820RlrZM7KHgdgA0UwUt83N4rrFrG1bS2ukFZvFNn1nhgETE2aQarNBJAKK8jodiSC8dR33QOXjH/84l19+ORdddNGUQGXTpk2USiUuuuii8rYFCxbQ2NjI888/f9hApVAoUCgUyr8nk8nj13jhDafCU8GFCy/khc4XGE+No+oqBgYW2UJTRRPnLzgfVVOxW+3HtuOuLnjsMRLFDNukcaJSDF3RcPb34e7ejiXazSmX/g1OqxMM0HQzgfdQ2aI5ZdqiHP6tl1BLbMmn6S6mqdL2Ua+3o+hxsrrObimAy7EUj2sFAYudsGJFOY49FKqm0jfZZw7bSGbAV9SKGBg4bU40XaOoFsmWsub9dZWSViJTzEzZT9AdJJ6Ls3toN22RtmPuVTEMoxxcOm3OV9UrE3KHuHDhhZzWdBq5Ug6rYiXkDh15n2NjsGED9PZCPg9WK9TWwqmnQkvL4R8nCMKrdlwDlXvuuYfNmzezYcOGabeNjIxgs9kIBAJTtldVVTEyMnLYfd5+++3cdtttr3VThTcJv8vPyqaVWCQLsiQzljan0Fb6KmmNtDIYG6QmUEOVr2r2O9V12LqVbD7NJm2EVD5FxBfBIlugAgo9nfQ88kc6fHDx8rcTcocYTY5SG6idspuiWiRTyHD23LNn/FDM6zq78im6E3105BI0yPvwKF3klACKUsmkUaKgTpJLPcSEOknStppam5MVDg/uGYKi10KmkEE3dHPqbskMUAzDQNr/nyIpaLIZrBgYVLgrKKpFNF2btq8D5yVXzOGyu2b1/IZh0Dneye6h3WaxNiRqAjUsrFlIS7jlVQUsfpcfP7OYgTU+Dg89ZPam1NSYQ32FAgwOmrddcgm0tr7idgiCcGTHLVDp7+/n05/+NI888ggOx2tXX+ELX/gCn/nMZ8q/J5NJGhpEdr7wsuUNyxmMDRLLxjil8RTcdjf5Up6B2AA2xcbKppUz9nYcVjQKw8MMWVXi6Ti1gVpkyRzmGYwNklAncI5Nsu7p3zGej1PtryaVT9E90U3EG8EqW0nkEkxmJllUs2jGoQ/VMNg1tp3syGYq04NUMUGVo5N+I8guKUNWGsFn81Lpi5BTPDSru/Da59JXCgMGq53+KT0rhmGUk4oVWaHKV/WK6pzo6IS9YXomeiioBTx2D3HNHAIqqkV0w0xKtlls6IZerk/id0wPAHRDP+Kw2KEMw2Bjz0ae63wOSZIIuUMYhkHHWAed452cM/ccVjSuOOZjOmbbtpk9KvPmwYG2OxxmT0pPD2zcCI2NYBGTKAXheDhu76xNmzYxNjbGqaeeWt6maRrr1q3j+9//Pg8//DDFYpF4PD6lV2V0dJTq6sMnOdrtduz2Y+y2F95SqnxVXLbkMjb0bKA/2s9gbBCrxUptoJYltUtwWB0MxgYJuoKz+2avaRilEkPZcVw2F7Iko2oq3ePdRDMxvA43EWcIv81DKp9CN3TqAnU4rA5GEiNohobX7uXceeeyrH7ZjAHDxMQujK6HqJKhyxHGKkXpS07QPTFATrEz7qwiK9tJx7x4wy00+lU8Wj819lqGSkXGbSWq9+dVxDIxXuh6gZ6JHjLFDLIkU+GuYFnDMpbXL59VEu8BHruHpoomssUsu4d3A2aejaZrFLUiEhJuuxu3zU1OzSFJEm6bmwpvxbR9RdNR5lfPn3XANJIYYUPPBgKuwJRieQFXgPHUOC92v0hdsI6INzLr4zlmqZQ57FdZ+XKQcrCaGrNnZXQU6uqOXzsE4S3suAUqF154Idu3b5+y7QMf+AALFizgn/7pn2hoaMBqtfLYY49xzTXXALB37176+vpYs2bN8WqW8BZRE6jhHcvfwXhqnFwphyIrjCZG2dizkcnMJIZh4Hf6WVizkFMaTzlyzorHg+F2I4+mUPxmImssGyOajRFyB3HmihQdNooOGz6nj0pvJdFslHed8i7sVjuaruGxe3DanDPvXyuSHXweMFB9zeiFHEOT3UjFAnZvNe5iAtmqoLmr0AspRkb2kLI3ELYnsUoyOhBVzUAllU/xyK5H6Iv2URuopT5Uj6ZrjKfHeWLPE5TUEme0njHr8+i2u1lYs5CRxAgLqxfSG+1lODmMqquEPWGyRXP2jyybdUqaKpooqIVp5evHU+MossKC6gWz7lHpmugiV8rNWM8k4o2wZ3gP3ePdxzdQKRSgWIRDhqjL7HZQVfN+giAcF8ctUPF6vSxZMjXb3u12U1FRUd7+oQ99iM985jOEQiF8Ph+f/OQnWbNmjZjxI7wmJEmi0leJYRg82/EsL3S9gM/poyHUgIRELBtjXfs6ErkEFy688PAJrm438sKFVOx6jnZLCr/TTzQTxapYUABPPMNQU4SMx47T6sTn9DGUGGIoMcTKpllMc00PQW4c1W3mtOjFLEOpFAt9djKGlYLuwF2IkXRX4XAFyCRGKKSG0YNnmccJaPvLxe8b2UfPZA/zquaVh7cUWaHaV41VtrJ1YCvzq+cfU3XcFQ0rmEhNMBgbpMJdQX2wnt3Du4lmowTdQdoibVgVK36Xn+ZQMw6bg2Q+STwbx6JYyoHLOfPOoSUy+8TTyfQkLtvhe7wcVgfxXHzW+3tFnE4zGMlmzeGeQx1IrH0Nh7cFQZjqhA6q/ud//ieyLHPNNddQKBS49NJLueOOO05kk4Q3odHkKFsHtlLtrzbLqO8X8UbwOrzsGt7FnMo5tFW2HX4nK1YQ7DyDwDP/i5GXULJpAvkCFaUS8coA26ptBNyB8rd7RVIolGb5LVsrYMeguH9IplDIMp51sDBYRFZLyLIVSc0j6RoFWSPitJHPp0hRg26YK9r4FAu6rrN3dC9+p3/GHJyQO8S+0X0MxAaOKVBx291csvgSgu4gv93wW8ZSY5zaeGo5TyVdTKOjM69yHhcuvJAldUsYTgzTF+2jqBYJuoI0h5up8EwfDjoSp9VpTvc+jJJWwm45zsPAbjfMnQvr15u9KocOmw0OQkMDHGG4WhCEV+d1DVSefPLJKb87HA5+8IMf8IMf/OD1bIbwFtMX7SNXzNEYapx2m8PqQEKic7zzyIGK203lu97LcNDByPOPU8plSFNicF4DPUEbit/HsprF2Cw2DMMwh3tmWyZfseNTbFh0lYKkYAE01Uu0YCPiGEPSDPKSFV2SqFVyBKQUg4Va3FIVca1EULFQpdjQdI1CqXDYHBBJkpAkiZJWml27DuK0OTln3jksqFrAi90v0h/tp6AVKKpFfA4fS+uXsrJpZXl4qzncTHO4+Zif52BNFU1sG9xGSSthVaxTbiuUCkiSNOM1fc0tW2YGJO3tZq6Kx2P2pIyOgtcLZ5wxPYARBOE1I9LUhTe9TCEz7YPuYE6bk2Tu6PV4ZI+H5Ze/H+9paxje8RjPdjyF3xuiqaKJKm8VAVcAMPMxgq7g7D9EPXW4PTU0pkfpckbAascq2xjLBFENG7VGJ16/m4I9Rd5wsSVZRcmzCrdkISDJLHd4cMgyhiThdXoZS47NuFLzgSnDh82VmYVKfyXvWPEOopko2UIWq8VKxBM5pgTd2WqqaGJOZA77RvdRH6wvB36pfIqB2ACLaha9PuvxBINw2WWwZQt0dkI8bg73zJsHK1aIJFpBOM5EoCK86XnsniP2IuSKOfzh2Q2FyLLMnJr5NFe2UVfTxqO7H6V7opue8R6QwG61U+Wr4oplV5QDl6NSrMg1q6jufQR7fowhh489ngCD0X48eTsJ7ypCjgVosp2eZJ6BQpo1c5aw3OGh3mrHtz+3RpKk8qrRBbUwbVhkJDFChaeChuCr/3APuUMzBkOvJbvVzgULL8ButdM53slQYggMc/XjFQ0rWDtn7REL5x0smUvSPdHNYHwQCalcOXfWvV6hEFx4IZx+upmvYrOZAcxJsFK0ILzZiUBFeNNrCDXgtDlJ5BJTclTADFIMjCMP+8xAkqTyqru5Yo58KY9u6DitTmp8NQScgWNrZHAOiqwQHnuJYGqQSCTCY2qGIU1GtzWRyFjJFFK4LTZuWngBZzSdMuPsmTmVc1hUs8hcy8YVwO/0o+oqY6kxbIqNNa1rZl1s7WTgdXi5dPGljKXGiGaiAIQ9YcKe8KxnD/VN9vH4nscZS43hsrkwDIMdQzuo9ldzwfwLqAseQ4+Iz2f+CILwupGM13NJ0+MgmUzi9/tJJBL4xB8QYQaGYfBc53O80PUCHrun/CEXy8SIZqMsr1/OBQsumPW3c4C9I3u5f+v96IbOcGKYaCaKgUHAGcCiWFhev5wbzrjh5TVjikXo7jbzHFIp8PvNJM3mZnMYodxYHXKToJfIGhId8XHW96wnlo0RcoVYVreMU5tORZblcsXYQ+VLeXYM7mD38G6S+SQW2UJdoI4ldUtedd7IG00im+APW/5AKp+iqaKpHNzohk7PRA8VngquPuVqsVKyIJwAs/38Fj0qwpueJEmsbl2Nx+5h++B2BmOD6OgEnUHOmXsOpzSeckxBimEY7BnZYy7Ul49jV8zhHlmSSRfSjKfHebbzWVa1rGJ+zXxzEbvHHoO9e82gxOk0K53u2QOLF8N555lTYAEkGVzmzKFiNk73RDdD8SHGUmPsGtzFuvZ1VHoraapowuPwUOmpZH71fFrCLeU8EYfVwWnNp7G0bmm54NtMq0m/UjldI6qpaBi4JYWgYkE+TkMgJbVUXtn4lbS/e6Kb8dQ486vnT3m8LMk0h5tpH22nZ6KHxXWLzRsMAxIJszaKxyOmHQvCSUAEKsJbgiIrLG9YzsKahWbvh2EQcAVeUWJpSSvRM9HDRGaCoCuI1+Et3xZwBXDZXOwe3k37WLsZqGzcCLt2mevBHFxVOZ83y7MHg+bMkYPkS3ke3fUoz3Y+SyqXQkdHQ6N7vJv13etpCDZw2dLLSGQTtI+1c1rzaaxpXTMlqdVutR/74otHoBkG7cUsXcU8aUMDAyySRJXFxmK7m8AxBHtHMxwfZu/IXnome9B1nUqfGZC1RdqOKXF3IDaAy+aaMciRJRmbxcZwYtgMVPr6zOsxMACaZgYq8+ebs35cb5zhMkF4sxGBivCGkc6n6Yv2kSlkyuvXVPuqUZTZr9tjs9io9r+6mheKrJDIJsgUMjPO7LFZbEhIDMYGzWGevXuJBl3kjTQhVcJxYDjI4TCDlN27zQ/Dg7699072sqVvC6lcCo/DrGq7d2QvTquTRdWL6I/1s290H5ctvoxMIcPGno1U+apmXEfotbK3kGV7IYNXVqhVbMiSREHXGSgVyOk6q10+PK/B4oidY508tvsxUoUUIXcIi2Kha6KLzvFOTm85nTOazmA4OUxnfJBEsYDP6WNJpJXIDLVhJI7cC1Me+e7ogEcfNYPHykqz5yuVgnXrzN6viy82e8IEQXjdiUBFeEPYPbyb5zufZzgxzGBskK7xLrKlLJWeSq5YcQUXLbiI+lD969IWRVYI+8IUe4vmSsKHfFvPFDN4HB4URWHb3ud4uOuP7LRlKA3qBCwezgkv4m2R0/DZXGagMjhoDjccFKj0TfYxmZkEyUwojWVipPNpfE4fiqzgcXjom+wjmokS8UaYTE+yd2Tv7AIVVTWfc3AQSiWzkFlzs5k3cxhpXaOzlMMvK+VZRgB2WaZOsjGgFhko5lngcO9/CpWtg1sZS45ht9hZUreESl/lUZuWKWR4uv1pNENjXtW88vagO0gil+C5jufYPryHvelxooUcGhKGYRDxhLl0/rlcVL8Ey0HXoz5Uz67hXTNepwN1Z2yaQfSxB3EVSjja5r58B4fDPDd790JTEyxfftT2vxIztU0QhJeJQEU46fVO9vLEnidQNZX+aD/bB7ajo6NICp3jnfzkyZ+woXsDHz3noyxrWHbsT2DooJVAtsDhegTUcSh2QKkHgEXBEs/ZZEaSI0Q8ESyKBcMwyBazxDIxKjwVJLNJvr3150zmuqm21+JRbMTUDL/qf5I96UH+oeWd+HTZLBZ20AdVSS3RMd7BvrF9OBUnuWIOTdfQdK2cPGuz2FA1lXQhTcQbwef0MZYco6SWsFoOXzOGTAaeeMLsQdA0UBQzWAmFYO1aWLBgxoeNqyWyuk79gd6gg0iAXMyws5CmWamnY2wfv3z+l3SOdlLUiwBEPBHOX3A+159+/csJxjPonexlPD0+JUg5wOfw8eS+pxkp5mmbu5a2QAN2WUbTNQbiQ/x+51+x2V2cH2ktf/C3hFuo9FXSM9lDU0UTsmQOG2m6xgudLzCUGCKxYwuDmzpJ10SYUxxgZdNKfM79iX1WqznLZ+dOWLLEPF+vgZhaYnchS3sxRwGdStnKIoebOVbHcalJIwhvZCJQEU5qhmGwY3AHRbVINBtlx8AO3HY3QXcQgGwxi6qp9E72cveLd9NY0Tj7+iVqHmL7KI5tJZ4YpISCFFpApPEsrK7gy/crdkLmCdDjIHsBiYX+KMsrYSSXYzw9jm7oYJjF1FoiLciSzEsDL5ExiqwIzjF7MZwegjYPWbXI+ug+/urZzLVGM0QiZqCAmZvy5N4n2Tm0k1g6Bm4opUqk8inyap6gO4giK6iaitP68lCEqqvYFNsRP+RUtcTEA/dS2rKJUkMdHn+YCncFiizD8LAZwHi9MxYwO7CW0KHf/EeifXQN76Yv1o+m63RJMi90PkdBLTC3ci4ehwdVVxmKD3HvxnvRdZ2b1t502DYmc0kUSSkHFAdL5BKM5+IUrC5qHN5yAq8iKzSFGugcbef5wR2cWtGETS3SPdHNvtF9jCXH2Deyj52DO6kP1eNz+Ogc72TPyB7sFjv1OQmLYiFRSrNu3zr6o/2885R3vjyV3e02a6cUi0cd/jEMg67xLrYNbCNXzFHhreC0xtOmrCY9VCrwl9Qk/WoeBQkLEj1GnpcKaU53eLnMW4EielgEoUwEKsJJLV1IMxgbxG1z80LXC0iyNCUQcVqdRItRKr2VdI13sX1gO2fPO/voOy5lMXofZbT3aboTY8RLRRS9hLXzCTZu+y1644XUVC6k0u2g3vIiVkkD2/zywytD1axpyvJcbxeV/jZstiAW2YIkSWSLWSQk4tk486vngzUG3T1oFpmErJNSs2S1Ivd2P8nZFW+n6txzwWK+FTf3bWbbwDZOaTiF/sl+JrOTRLwRrIqVjvEOJtITuO1uFEkh5Anhd/oxDIN4Ns6atjUzTlcGMzn1oUd/geuBB0m4LMhaL36nn7pgHUvrluKtrTWnTu/ZM2Og4pBkJMlMqD3wIdo72s7GfevIqHkkZ4CgxcbmvY+xc2gnyxuWlxOVLbKFxlAjsiTz+N7HuXjxxdQGamdsp9ViNYO+GcSzcaKFLBFv1YyzjCrcIQYme9mXGKa/ez0dYx2MpcYYT40zkZ4gW8gylhxjUe0icoUcPruPpnATlXIOjy1HtS9IUVPpnujm+c7nuWzJZeaO83kzCdp6hJ4qIF/Mc9dzd5kLXeYTSIa5ZEF1oJrrVl7HRYsuIq/rPJqO0a/mqVFsOA5cLwNGixmeTkcJSTJrvMe3mJ4gvJGIQEU4qem6jm7o6LpOPBvHaXVO+VYvSRJI4LA5SBfS9Ez2cDazCFTGtzHWu45N8RiKxUPQGyBdyNA51oFl8iX6RrvZWH8eFY48ze4xzp9/GYGDPqckSWJ1y5m4LAV2xCxEi+YaOn6nn+UNyxmOD7OufZ257k5VFYV8hu6BvcSLKSRZpqQWaJfj/CYU5zK/wULM/Iw9Q3uIeCOEPWFObzmdh3c+zEBsgKAziNPqpHeil8aKRnOhv4pmPHYPPZM9hNwh5lfNn/FQh+PDfPOv36SwZRNrc1mKoQrUfIaSWiJbyGIYBqc3n44tFDJnvpRK0z6UKy1WArKFSa1EpcVGNp/huc7nmdCK2LzV5AwNydDpTowR8VUxmZkk5A4R9oTL+6jx17B9YDtb+7ceNlCp8ddgs9jIFrPTVk7WdI2iWiR8mGRoWZbR1SLrO54nF+2lpJWIZWIEnV4agjUUNI2RxAgDsQG6J7uZWzkXl81FssJCzu3AnchCwI3fGaB9tJ01rWvw2z0QjcJBweTh/Gb9b7h/+/3U+GtoDjcjSzIltUT3ZDd3PnsnPqePUN0Sekp5wrKlHKTEs3HGU+Ok8ikSVjt3j3fQr+aocAZw2BzU+GtoCDYceUhPEN7ERKAinNTcdjd+p5++aB+yLKOW1Cm3F9UiFtmCVbaCxBHX9Ckr5VDHd9CdTiIpNkLuEPlSnp6JbnJqkVCwmdrMONuTAzgMg0eHuhnKrOOm0y/AY3+5699isXBq/XwWNdQyYZxuFnxzBfA6vDy4/UEwzA9XWZLpc+rEKjwE9CAKBnopjdPhRGlp4cn2dfhcAQzDIJFP0BJuAWB5w3IkSWJD9wYmUhO4bC5KaglFVgi5Q9gsNnone6n2V3P23LMJe8PTDlXVVO7dfC97R/ZyQbiFwHA/2v7nSufTpItpeid7aapool5yga6bP4ewSTKL7W4251MMlop0jnfQkRwhEGxCR6faYsNWzJFUC/isDpySzER6YkqgcqC3J1/KH/bSVPuqmV81n60DW6kP1penfhdKBXM6uDuA3Tlz0m8yG8fq8BNNDFHl8PJS72YClixuWYMiOGU/XqtEvFAkmo6iVpivpZLDxnBrNS07e5GjOnmng2ghQXpyFH92xFwd+TC5OwcMx4d5at9ThD1hqnxV5e1Wi5V5VfPY1r+Nx3Y9xtmReRQMHbds5umMp8bpmezBMAxcdhfWUpGuXIzubffT5ArSEmlBkRXaIm2cv+D8l3Nn3ij0LGgxQAKlAuTjvNq18KYkAhXhpGZRLCypW0J/rJ8KdwUdYx2EjBCSJKEbOql8irA3TEEt4LK6WFS76Og7LaVJpYYZLxap8NcAEMvGiOdTWJwBukpZ3LlJtsc24w2G8JLj4T1bMAyDdy09E6fNhkWW8TpcgIbD6qTeM3XG0fKG5VR4KhhODONz+ohl4/j8YRTFiqEbJCZTnFa3iJZwC+2j7ewb2ce86nlImMelYH6oL6tfRmukleH4MLFMjHguzuXLLifsCSNLMl6Hl8ZQ42HrpQzGB9k5tJOIN4Km+dAtCkpRRbNZ8Dq9TGYmSefTTKQmqC95zboh9pn3VWe1Y5Nk9hQyPJlLARCw2ggoFnyyArKFgN1LNBfD466gUCqgG3o53yRfyqMoSjm/aCayLHPOvHNQZIX20XaG4kPmcgWywvL65QSDTWyND1PpDGA/KLE1nUuQN3QafZXIE13kS33ks12EAwFg/2wqbRy3rDFWVJBlM5BqrDCnl482V6HLEtU9Y/hGxnCpBSzJFCw7A1atOuKMKIBdw7uIZWIsqVsy4+3V/mp2j+xmeS7GgVLgBbXIYHwQi2zBY/dQUPMk8gkskkJL5RwySfO1E/aE2Te2D1mSefuytx92eO+kYBTNhPP8bijsAD0KshPkEFgqwbYEHEtBOomPQTjpiEBFOOktqF7AWGqM0cQoe0f20j3RTcgdQtM1vE4vLouLifQEa+espS0yizV7JAUdCUkvYZHNt8BoOkpGkpHVApnkGE5Nx25zo7ir8crDqBis69zBntF+5lXW47BaaQpUsCRsUFd30bSnqA3Ucv6C87lv031MpicpaSWsipV8Mc9IcoSgO8iKxhWAWSSuN9rL6S2nE3KHmExPTqn14rF7mFs1l6H4EAsdC7lwwYWzHgaYSE+gqip2i52U30s84ic0HCNaE8CQZewWO+liGmJx8AfMQOUIIhYrad1Bvd2FarHTaLGZybgAisKc2gU8t+cJooU0XndoSh2T7olu6gP1nNZ02hGfw2lzcuHCC1nesJzR5Ci6oRNwBqgL1jGeniT/0l/YNboXr92L22Ijm09RkGXmN67kNH8NL41tRS12Iyl2UAIH7dmBXpokZMsRcHqYyEyQKWRQNRVFVtCaIozW+Il2qsyvaMNx2XuhsW1WCw+WtBIGxmGDCKtiJV1IEwackkxK1yjmEuRLufLijol8ioJsIaQV8cgyWSSG4kPUBmpprmima6KLofjQ67Ni9Cuh5yD7OOR3mgno+gQYVpB0wGHOqEs/CK7zwHsNWEQejjA7IlARTnpWi5Vz551LQ7CBCm8Fj+x8hInUhPkttFQgLadZ07aG965+7+zWbHEEsPga8Q9sJVfM4bA6SOoqmgFyKYdXTZFWXGQkLxXWClJqnMFkB27Fi2FAa7gGl8XJzqGt9MQiXOw2aK2a/jQ3nnEjkiRx38b7GEmMkC6kUSSFukAd580/r5yncaDHQZEUGkINPN3+NA6rY0rScDwbJ51Pc2bbmcecq+B2uEnmkhiyRO+iRhRVIzQcQ7VaULQ8rmSCYFiB1auhpeWo+ysZOpXBWmIOH9l8Eu9B7ZxTt5Se8R66hnZQZXUTz8UploqMpEYIOALcuOrGWa1YLEkSEW+EiDcyZXu1r5KPnf5unhnezcbhPaRKOWpDDSyqmsfqyjl41AJd1hwTmQJgQzeMKYm3qYJMrddORrWwabCbdfvW4XV4sSpWrIoVi2IhWB1m8ep34G+affG8al81doudZC454/BMNBOlwlPBYl81XfkkL+UzWNQiEhISMhoGcQwskkwgPY5s6DisDlJ5s+fKYXVQ0kqMp8ZP3kAl8yRkHgYtbk7lxw5GFowUoIFlHlgikH4AjDx43wXWY1gQUnjLEoGK8IZgUSzMq57HF9/+RT5y1kfY2LeRkcQIVsXKotpFLKxZaCauzoYk42tYS6j/RQbi3aiBJlAcSFocp5pGkg22anbcgTAWq4uxjI/JnMbcOhuyUSKbG8QfqMNftYDOdIC/dKxnpbMSw2LDLyvUWGyEFSs2i433r3k/S2uX8rNnfobP4SPiidAcaZ6SSzORmsDr8PLHl/7IRHqC8fQ47aPtRLwRGkONqLqKRbawqHYRDcFj+5AKuUJEPBGKpSLRTJSQN8S+0+YSHI0THIkxMpHAtXQBvhvfD3MWz6r3wCrJeJxBWmoXsatnA6qu4neFygslNlQvIOKrIqirxDIxLIqFs9rO4pLFl5R7kV6NoNPHO1pX8baWM8jpGpIk4ZZkM7Ha5mBBxM9TcQsWRSOeyxByecyZUbkMGgaTmTxdk5O47C5ypRyFUgG71Y6qqwTdQS5fejlnz51FQvZBltQtYX71fLYNbGNJ7ZIpa0el82mS+SRXn3o1bruTt1ksFA2dDXmFhM2NLlswMJcZ8kV78eXigJlf5LG/HNRJkoTBSbqGbLETUveCnjZzUrQJcxjIyIHkNIvtlLYDc0FyQ7Edsk+ZwYos1lMSjkwEKsIbTtgXfnnq6CsVaIXGC4ht/DmZ9kewGjrBUp5JVWOn5iTur6XRH8KnxBnJ9uL1VuPzLSabHcewVIFrJQW5ggw59ox1YZ3sob6ihWEKdBRzLLC5WGB3IUsSyxqWcfGii+kY66Al0jLlQ2wkMUJftA+f00ewaFZfzRfzJHIJRpOjaJpGW6QNDY3O8U76o/00h5tZVr9sVksB1AfrWViz0JxRkkswFB/CZXMxHrYyYtOxNrfwd+f9HcG5M+dWzKTSYsUjK7gaVmBTrHQO72JwohsAi8VOff1SblxwHvOcAaKZKDaLbVZVaY+VRZLwzrC+0BnNSygWxsh1j7JztI+B+AQuqx2b1UI6n2dXbBCrNcAZzWeTzCcZjg/jdXhZXLcYRVbwOX3HtEglmIH0+1e/n+898T22D24n4AqYPXW5JPlSnrVz1vK2JW8DwK9YucFXRYum8sfxThRDJ6RYScT6GB3ehRyoMavmqgXqgmaPQ0krIUsyQdfh83tOGEOD9KOgjYOlGfLbzYDFMPYHKlnADpLFzF+R3WbeSrEL1H6wzT3KEwhvdSJQEd6StvZv5emxIfKR1WiuBrpiA8TUCXZnYmiKzNsrdZrc27CRpLEuBdZK7NYi2/J+Av6lGEoNvcUccQN8ioJXKxHePyST0TV2FbJ4FIU6xcpgbJCAM4BmaGzo3kDEF8FhdZApZMiX8tgtdhpDjewZ2UMsG8Pv9LOgZgFD8SFe6H6BwcQg5y84n0pvJQW1wI7BHQxEB7h0yaXlD7IDM2nsFvuU6dsHhs1UXWXn4E7iuTjxbJyiVqQp1MQ7lr+Dc+adc0znzi0rzLM52ZpPE65bQm3VXJKpCdJaCdXmYnGwjkUuHzZJxmV//Rfzszvnc35rO4vqVrChv5u9Y4OkCjlGklHykkFjMEh1xan4XX78Lj81/hpGkiN4HV6aw80MxgYZSYyUz+1sza+Zz+cv/TxP7H2Cjd0byak5WsOtnD3vbM6bf96UYUmbLHNWqB59oosNPRuo8FQQcfqJKRZGk6OUtFJ5LSvd0Omd7KUuUHfMPWqvC3UItGHAsT+BNr4/SMkAecxOIA1wAqp5m56GUieoUTh8oWJBAESgIrwFjafGebH7Rdx2N83hZnRjBaVsAk+pQHCyCzn+v9RZOhnP+CjJlcSzNuYEZGot+1DCNUR8YTKGTlRX8UoSOQMsyst/bd2yQlrX2JkcZUv/VronulA1FV03V0AeT42zoGYBpzScQu9kL0PxIQZjg8RzceoCdeVAQ5HMCrSFUoFENkFjqBGH1YHP4aNrvIsXu1/kFPUU9o3uYzA2iIFBtb+ahTULaQm3lPcT9oZ5x7J3sLRuKb2TZn2RSm8lbZVt03JAZmuOzYlNluks5IhZQA7UUi0rNFjtzLU5sc1QWfZwSmqJVCGFLMn4HL5XX0Le2oJsb6VG7uHKRUuJL1zN9kSCR/ZuopUkXUPjOO0vV4q1KOasm4HYAC3hFlRdpaAWAEhkE0xmJpGQqPBUHHV6cGNFIzedeRM3nH4Dqq7isDoOm2ArSRJntp2J3Wpn5+BOErkEbpubwfggXqeXal81o6lRsoUsdUEzr+mkrKWiJzGjjSzoMXNohzgYKqDs/yntv28BpBRYGkEbAH3yBDVaeCMRgYrwltM93k0qnzKrxgKyJNHkcFOUZSqqK8nlYf2IQsEoghwnX0zRP5YlU1fPxXNtZG1p+lQbJV1HzyfwuvxEAjVTnsNp6Kzb+xSexDBzI63l/JmSVqJnogerbGVp/VK6J7oxMBhLjRFyh8rBRUkrEc1GcdvdOG3O8myPdD5tJqiqRR7a8RDbB7bjc/io8JgfvB1jHXSNd3Fm25mc2nRqeX8uuzl1e1bTt2dBkiSarA7qLXYSuopugFuWcR7D1Nliqcgjux/h6fanGUmMlPNwLlhwQbmGzCsiO8F9EVrmaXblJ+nWDAZUlQl3DX73CrJSFDkTI8LL9WJcNheJXIJ4No5VtmIYBk/tfYp9o/tI5pIA+J1+5tfM5/Tm04+aD2W32rFz0DTvUg5yY6BrYPOBswIkCavFyurW1SypXcJYagzd0MkX88RzcfOcKBZaI620hFvKNWVOPsr+pFkLLwclMmZXigHl87z/35K0PxdKQnwECbMhXiXCW040E532QRNUrMy1QffoLnqTBZyeGpoDVWjFDA49wmhygq5EnrFMnkr7KJoeIJ4aw6EWOGXOWbjsU2eyjMUGGYv2saRqHnaLvTxrBwkCngjbJ3sw+rczIEn0Z6KkS7lysAFmIqWqqciyjNPmJJaN8VyHuYaOLMvkijm2D25ncc1iLl9+eXldmqA7yGR6khe7X6QuWDerPJYjyRayJPPJcpG5Q3sHFEkiNJsie4color86Kkf8fiex7EqVkKeECW1xFP7nuKl/pf40Fkf4tz5577yhisB2m3ns0sdIWDNUqfH6THyVCiVJNwSo7qOW83h2j9kphka0UyUu1+8m8n0JD97+mfYrXZWt67mgvkXICsy8Wyc5zufJ1fMceHCC2dXz0TXYHwbmaEN6LkJXBYris0D/haoWQ2OAAAeh2dWs6FOSpYqkKxmMTdrE6iD++ukWIECZuBiM3NUJI/5ow6aBeCUV9ajJ7y1iEBFeMs5sPLwodyGQTExToXdQXWgitZIC3ZZRkYiU8iwsW8X24YnWCJ1MJF34LQ6WdB0GnPrlk7bV0+sH5ckIxs6m3s3M5IcoagWKRg6KVmhiEzJW0Vj9XyGujeQ1Er4SnlC+9fHUWQFbf+MFqtsZTA6iCzJNFaYa+YMRAewK3Z0dLb2b2VV6yrsFvMbfIWngomRCTrHO19xoJItZHlp4CX2DO8hnU+jyApV/iqW1y9nTuWcV97bsd/THU/zxJ4nqAvWleuIgFl/Zt/YPv7f+v/H0rqlhDyvrNZGTtfoKhXwWoN4lQgOSy0+V5RUPkmDy0+qlGcgnqKmkMZpdfBS30vsGNyBYRhUuCvIlrLoWZ37t97PaGKUv1n1N4TcIRxWB7uGdzG/ej5NFU1HbUf/vgfYtet++vN5NIuboMvLIr/O/MJWrMUktLwdbG/QAOUAJWgmxOY3gNwMlmpz5o8SBC0JkmwGKYZhrlAuKSAHwLbALAInCEch1hMX3nKaKsyy70W1OGV7IpcgmitS4bBQ7Q7hlBXk/QXL3HY3K5uW4HEEmFu7hhtOvYYbTruOYM1CSod8Zsf356MEZJmtA1vpjfbicXioCtSAO4wmW8klR5gc3cuicCsrG06hqBbZHe0np2vohk6ulEOWZJxWJ5PpSZApBykA4+lxvE4vLRUtTGYmGU+NT2mD2+5mIj3xis5PvpTnsd2P8Uz7MwDUBesIe8MMx4f545Y/sqF7A4bx6qbJrtu3DkmWpgQpYA4ptVS0MJQYYkPPhle8/5imktY1s2IuZsG1lnALuVKeolqizV9DMNxCVtfYPbSbHYM7UGSF05pPo9JfSdAVpCnchNvhZkPPBp7reg4wh4h03UxunUm2kGXfyD62DWzjkS2/54/r72ZXOoXdFcbrcDOZTfHwwF7WpfKo8S6Id7ziYzypeC4D2yIgAbIPJMf+gMT58u+WarAvAVubGaDYF4Cl5qi7FgTRoyK8+Rk6yVyMMU2lqDiQvBFqK1rpGmunPlRfrlWRKWQYzRRYEAzgd0wfzrAYJewWB9UVZxAJtVCr6yiFNH3FPDrmMEjJMHDLMqcH63ii61kmU5PUBGpQZIWEppIzdMIOD6rNSTqfIpWLccbC88kXMzy682FeGthBnSeI3+Hn1KZT6Z7opj/aT22wFlmSUTWVeDaOJEmEXCGsFiuKpDCZnqQ++HIZ/5JWKvewHKuOsQ52j+w2y/TLMiWtRCKXYCQ5Qu9kLzsGd/D2ibezsnklbZG2Y+5dKaklhuJD5eGqQ9ksNgzDYDw9PuPts3EgO+LgYm9NFU1oukbXeCfJbBSrxUZFoI5cJobT5mR53XLsNjvJfLKc0Bt0BUnlU2zq2VSurWKz2MgWstOec9fQLtZ3r2c0OcpkZpLu3ucJl2Kc0rwav8OFhITX5iRbKrBtvI8GSxPzJndDZPlh69eomkpftI+eiR5ShRQBZ4DWSCt1gbpXn3T8WlJC4Hu3WXlWCUKxB2QX6Bko9Zm9Ktb5QMEMWhyngutcc7sgHIUIVIQ3H8MAbRL0PHpymH2xUfaVimQlBdniQHdXI9Uvx4fBZHyIodgQALqhUxucj8frxmJMgFYCyWvm/Olp4plRfN55+DzmAnV2WWalw0uT1cGYWqRoGHhkmWqLHalmAY9oKql8ClfOhaqrJHQdzeIgUyzidvjw2H2MxYaoDNRx2Rk34I+00dWzAbckYTgDGHY3C6vm4+nfQjw+yGBsEFk2a2msallF90Q3Ja1kFgI7qIejpJUoqSXqAnV0jHUwkTJ7VsLeMI2hRmyWw88HNQyDJ/c+SftYO13jXWa7swmyhSwRX4RKbyUjiRG2DmxlJDnCWXPOYmXzymO6PJIk4bA4iGVjR2yHTXnl81ZdsoxdksnpWjnBV5Yk5lS2URuopTM1jkXXWNO4gnuKGTb1bsJuMwM7q2JF1V8eGnRZXYwkRyioBewWO/lSfkrVYID20XYe2/0YiVyCWDZGX7SPgdQkeT3LSOd6VjcsZVFl6/792VEkhX3JKPPCBbMOiTT9T3G+lOepvU+xa3gXGOYK4Z1jnWzt38ryhuWsnbP2mOu9HFeOU8HQofCSWexNGwLZCs5TzfeRkQOlEbzvAOcqkMS8ZGF2TqJXufBWF8/G6ZvsA6C5ohmf6xWsFFvqh/wWUAcg2cVkcoQxpQW3sogKyYdUjEN2mGRgHrSsoVnN4Sma344rPBXsGNzBlr5NBN3VWPVBc1E1IK/Ziet1XNB8FXbbyysoK5JEWFYoZKKkC2mQFSR/DT6nD5/Dx3BimL5YH1bFSlbXyRk6tf4alrWuxsAgmY2yt38rI9E+YqpKsPl0wp4QNosDp2LF6fDhrV1M194nWOz0Y7dYCTgDgLlydO9kLyW9VP7gzBaz9Ef7qfBUsG1gG6PJ0XIQI0kS9cF6zl9w/mGnJe8Z3sP6rvVohkZdsI5cMUdnptNcDyejYGAwmhzFajFnxvx1119pCDUcU0E3WVZY2bySezfdi6Zr05JSJzOTeOweFtctnvU+DxWQLVRZbPSV8tRKMspBPRaK1Y7fV8VpTi9zbE7cNveUiq8eu4dYNlZen8nAQJZkFEkhno3jtDlpDjeX76/pGlsHthLNRM0ifYaGy+Yi4PJTpSsMFIq8MLCLoDtIzf4FGT02B5OZSXSrB/kwC/Rt7NnI1oGttIRbpiR/p/NpNvRsIOgOsqx+2Ss+R685SQHXKrAvBOdaKHVBacDcLtnNIR/bArOMviAcAxGoCCdctpDlvs338VT7U0ymzJoVEV+Ec+edy9WnXI3DNssS26U+SD9sdjfrHrRkhlFrmJAygY2txOUzUC0VoObwJTspOkMkXZWcGm7Cur8L2m13k8wn6RzrxG1rwqFopItZSpqFZc0rWFK3AqJR6O6G8XEm8wk2aCO0O4oULeY+/E4/YU+YeCaOzxnA4fKTzCWRDA1JB1m2IMsK0dQo2Xwam3UvTruLYZsXOT2Ono2xvHYRDfs//B2+KgarFhLLTrLkoOTS1qr5dCTHSOBgj8XOQHKCkKHSWtFMKp9kNDlKc/jlcv0HpkY/vudxrlx+Jc6DAi4wA59NvZvMuiJWD3aLndHkKIqs4LV76RzvxBFzYLPYKKpFRlPmIpF1wTret/p9RxwCMgyDUa3EQCnPpKoi1S/H1fEM24Z20lbRXJ7xMpmepD/az4ULL2Rh9cLZXfcZSJLEEruLvK4xqBZwSQpWSSJn6KgYtNkcNO1fcfqUxlO4Z/09JHIJ/E4/DquDoCtINBOlUCqQzCdZ2biS4cQweTXPma1nUuUzF3fSdZ2dgzvZ1m8GhaquUhuoNf8tO0DWqJct9GWz7JnopcoVQJYkiqU8AclAD86jb7LXDHB0jZA7RFNFE6qusmdkD5Xeymkz1DwOD16Hlx2DO1hYvfDkq62i+EBZAo4l+wu/FfYHKydZO4U3DBGoCCdUUS3ywyd/yON7H6fCXUFbZRsYMJwc5u4X72YiPcHfn/v3WCxHeakaGuQ2gJ41v7nFO8mqJZLOeryGipVBXHoXSWUFWJyQj+HPjjLhqCChaYT3Bxleh5e3LXkbXeNd7B3ZS7qQptnfxvzq+bSGW7F2dMIzz0AsRlrS6OjfhjOb5rQ5c0mceRoFr4toOsqfXvoTaTR0T4ii3YPPV4VuGDgMnXw2zq7eLSiyRENkDg2RNmKA2+GnzmpDz8bZObgDn93LSKyP53Y9xtaBl8hkkzTa7CyqXkjJMNg+0Y1usdFUs4iwqwJ/RRMBdwBLIcvk7r8yt3LOlN4Kq2KlNdJK51gnPZM9LKyZGggMxYcYTY5SF6xjW/82CqUC0UwUGZmJ9AQGBqqhUumqpCHYgN2iUCyM8+Ke33FevUZjxSKwtUybyWEYBvuKWXYWsmj7661UhBqYs/AS1r14N0+0P11ehK/KV8XFiy7mg2d9EFVXSefS5It5cqUcuqHjdXip9FbOKj/Dq1hY7fIxqBbpLeYpGQaVFiuNVge1FjuW/YHV+fPOZ2n9UtZ3r6cl3ILP6TPzc5DpGO/AaXWysGYhEW/EXNOnaj6SJDEQHWB993p2Du1kffd64rk4EY9Zddjj8GCxOEgYMkE9SlDWmMxMkson8EgayeQgy+adx//276Mz2o+ma8iSuVZStb+aOZE5JHIJ5lTOvDBiyB1iLDlmPucrLNr3upAkMydFEF4FEagIJ9SGng080/EMbeE2/K6Xkytbwi1Mpid5at9TrJ2zllObTj3yjtRRszaDxVyRGC2HIZvf4CRJpmQEcDBE2piHLrnA4kAuptGN6cu8OW1OFtctZnHdYgzDeLmnYHgYnnrK/Pf8+fQN72YwZKe+qQn3wCjKxu2MnXMGdqudlFYkDlQF6kkkBtHVPA6rC13XiJXyTCRHqA8344+0MalrKBYHAasDHwaSK8hgfIDfv/ArNux6hGQ2hsPupqTrbB/vYGvfS1RH5tLSvJKWUAMSBpMju/BJBjWhtWyMjyA5AzPW+VBkBYtiYSA6MC1QGUmMsH1wOwDJfJLB+CAlrURRLSIrMlbZSlEtEvFGsFsMKLbjYBR0G+1DW2h0TUBhCzjWgHN5eb8TWoldhRwuSca3P+DsGt5NMT5AU91StEgbfjVHSS3QEGxg7Zy1tI+2s3NoJzuHdjIUH8JqsVLtr6bWX0tTqIk1bWsIe8NHfk0ATllhjs1Jm9VRTng+lNVq5avv/Cr/50//h2392+iZ7DGvuQEtkRY+cOYHuGL5Ffid/vI5HYoP8fDOh0kVUjRVNNE13kWmkKGgFega76I10kqlt5LB+CBZyYPbKiHpOqnUGCPI1DWsod9WTf9ED83h5nLis6Zr9EX7eC7xHLlS7rDHVR7O49VNExeENwIRqAgn1IauDeiGPiVIOaDCU0F/tJ8NPRuOHqgYBTBKL6/EKttw6EXsGOSRsGDHYmSRKaLjAq1E2uHEJSt4jvDtfMpwxr59kE7DvHmomspQwlzMDouFbG0lruFxHGOTRAM28rKCWsxT5Q1T469kNDZIJp/EhkRrRSMjGDR4K6my2vEoChaLnV5Zgf1JnPHkGI9v/j1Oi525dUvQgSKQkyT6JnuYSI9xXqiJyoA5BFFUC3QN7yLsr0YyNDJ2DwbM+DGmyMqUZFGA0cQo922+jx2DO8w1ZvzV5W/t7WPtSJJUXnum1l8DpR5KxUmQvTRW1DOQkdEt85D1Scg/A5YgWBsBGCwVKaFTpZgfxolMlO3d67HIFpbVLWZSU5lvd1FpsTEUG+KXz/+SgCtAJp+hN9qLIiuk8ikmUhNk8hm6xrsYiA9w4+k3EnAHjvy6OOg6Hqk8W1O4iZ+972c8tucxNvVtoqgVaQw18vbFb6c2VDvlvoZhsLV/K4lcgrlV5oJ6taEGdox3oNjcFNQi7clxFlXOQTcMOsc7yFuC6A4PqcBCFtYvpSHcxpP7npwSpBy4Ng3BBp7reI5EPoGu69QGa8szvA6YzEwScoemJfUKwpuRCFSEE2osMzYtV+JgdqudaCZ69B1JdnMMXM+bwYqzApusEFZz9FjcuCmiSzZ0bKCrlHSVhKOSxVb77Mq+Gwb09UEgAJgzhNT9K9rquo5ssyKpGrZECqOiBiwOpFyKYimP1xWgrqIJi8WO3WInmp5kLNoHhRTeQpKwO4yOhN3QyUsyTkNnT98WsrkkrS1nAKAioxUzqKU8NYF6+hKDdAxspjpgrshrs9hxO7x0j+ylpn4pI7pO0QC7dOhhGORKuXKOBZg9KXc9dxfbB81y/OlCmqJWND80KxrQ0eka70KWZZormrFKBbK5MaJ5g8ZQBL/TjSxJZlBniZiLzRX2gLWRoq7TU8xOKdg0PNlHOpegaf+HvIRZoA2goBXYOrCVoDNIX7Sv3Kug6RqqptIx1sHimsX0TPQgIfGhsz70muVo2Gw23rbsbbxt2duOeL9ELkFftK98Dgu6hrNyDq7JPkYmevC6g4zmk8iFNB6Hl9NbzkBGYln9Mi5bchlVvio29GxA1dRpU8gnUhOs715P90Q3sVyMTb2bcFgchL1hTm08ldZIK5IkkS1kOXvu2SfXrB9BOE7Eq1w4oSpcFewq7jrs7Xl1+lTQGVmqzGEfdQjkFnAEwdtEbbyTnLOSvCXBOHNIaQqFQpySt5EmbzXzbMewuu/+7vZMIUN/tJ+eiR7S+TQhT4iwJ0zT/l4Km9WG0xWgZ2Ar7YM2rPunA1utNlS1RDw1QUmxMBQd4Ln9QymLahbhdgXp0lS0TJSu4T0Ykpkb4rC7sdjc2NQCaUPHbrGiSDJj+6dVH+Cye8nlU3htXjx2F8OJIZoDU3sDBuODVLgqaI2YU2WLapGn9j1Fx1gH9cF6rIqVrokugHJ9llp/LQOxAQzdoKSXGIz14KBAa7iVhVX19EyOsrSm6eXeJzlAsjDMY9owG3I5+tUCBgatFgdLHW7imQnsByWIGoCMhKqpPNv+LPFsnEQ2QTKXxCJbSOQTSEh4HB7ShTSjqVGaKpp4at9TLKlbwllzz5r9NXwNFNUiRbWI3WMGGX2lAgkk1rScwVZDYzQxhFrMI5UKWAM12CWJFVVzuWzJZeUCd4ZhlIv3gRmI7R7ezVN7nzJzWXIpClqBgDNApphhcmSSweggTeEmljcs58KFF7Ko5rVZt0kQTnYiUBFOqFWtq3im4xmS+SQ+x9TpyNFMFLvFzunNpx99R5ICztPNWT/FTrPiZWgeVrnInPRu4gUnXZKdgjVHwFdDQ2Qp1c7A7Ff5lSRoaiLz3Do2xhJMpCfwu/ykC2myhSy9qQ7kvAXZZUMvFhgb66SklcgV0rgc1VgVGz2jexiJDVIXbmP1wouQ44NMJIfLibtOm4feQpacw4Uhy8gWO0XZQj6fwVfI4EKipJegmEPVShTU4pQcGlUvIcsKht3NGdXzmUiPs3dkb3kxu1TeLBh2zvxzysFff7SfwdggFZ4KErkEFZ4KJEliNDlKKp8ino1jt9hZUrcEt83N6c2nYzPG8dGFx9PMQHyCgMvDvEhd+VQlNZk78rXsMWJYJRknMglDZXcpS79WpN7mQdsf1KmGgSyBV1EYig8xlBjCrtiZSE9Q0Aok8gkKpQISErlSDqfVSW+0F7vFTl2wju2D21nesPx1XbDPZXPhtDrJFDKossVcRVtWcHhCrJp/Lr2j7fSPd9IcqEF2BWionMelbaunVOENuoMYhlGent053sn67vXl3sNYLoZVtjKSNBdr9Dl9ZItZErkEsiRzauOpJ1fBN0E4jkSgIpxQZ7ScwZrWNTy17ykqfZVUeisxMBhJjBDLxLho0UWsqF8xu51Zm8xS3rkt5hLyRgn8NVj8Kwgb9YTlENiD4Hxl68cYc+bQ/ejvyKVGqWueh4GBhMR4bJiqWJZdXoV94xsY6RlnLNqPx1+D1eoglU1gVaxoukZb9SJUqw2frLCgYTmP755kX8xcodeqWFFkKz5vBQVJYTQXw1mMYEUjlZ6kICvmh6OWQC3lyalFdvRtZm7NQhw2F/H0JDW1S3DbXKwOVKN4w3SMddAXNWvTLK9fTltl25RZIvFcHN3QqfBUMJocBSDk8hFwuMmWiiRySWyKjbpgHV6HFwODbEkmX8oxlOkm4o1wTttiIp6Xc4weyjvZrQeotthwKRZUdCyaRF5VSesq/d4ILt0gVyqSliQqLTa8soVd8QEsssWcFqwW0A1zmQObxYaEREEtoOoqVouVnokeqv3VpPNpRpOjr2ug4nF4mFs1l/U96wlZXZR0Hd/+4SenzY3X6ee8Ze9g5bxz0RUrmtWO45AcrKaKJmoCNfRN9hH2huke7yadT5NX80QzUUpqCV3W0QyNjJZBkRXC3jBOm5Ptg9vZ0reFM+ec+bod83Gl580CcbJTTGEWZiQCFeGEsllsfOz8j1HhreD5jufZO7oXCYmwN8z1p1/PNadec/SpyQezNoKl3qxMe+CPn/JyYJLOp+ke2E5ftM+sROuvpTXSSnB/Ia6DJbIJOsc76Y/1YxgGHpuHvkYni7v8eHqH0B12FqoO6lU/ndVunotkGBreSUOwgSXVC3CFGhjLJsjvX33Y660iGGygkB7DkkuAO0hJL5lTYSWZeC7OqY2nUuWrYjQ5ykPxfkbH9lEfrKdQypPSirjtbkYSI4RcARbVLmQwOkC8kCXorcTh9NFUNY8VDg/VFht4I0S8Eda0rTns6Towa6TKV0X36A4Sie347SoyBh7JScliJ6sa1PhruOqUq0gX0gzHB9EyboLWMZoip+I5qCcsXxxns96EXbbj2p8/YUEmoFiIA5qqkpQUrNVtdA3uYm64lRarff8yB0l0XaeklfA6vCRyCbPYmiwjISHLMpquYcGCJmkYhoGqm+sqvd6WNyxnODHM9rEOkjYXbqePkloglhon4A2zvG0NfneQrK6RM6a3z2F1cO68c3l096Ns7t1M+2g7PRM9JAtJNFUrT1d2WB2okkoilzBrtPhryRazvNT/0hs7UNHzZi5TcR+oY+Y22QW2hWbBONl9YtsnnFREoCKccB6Hhw+d9SGuWnEV3RPdAMypnPPKZzRI8ozVL4fjwzy25zGGE8M4rU4kSWLP8B629m/l3PnnmjVc9uub7OPxPY8znh7HbTP/aPZH++nS+7CeuYamlI49mkC3KJSqT0P3gq/nebyGQaWvkmwhS8Bqx+cNM2F30TPajk2xUGu1kbe5KBayPNvxLHtH9uK2uSmoBfLFPLFcDL/LT32onqV1S2kfa6d3spdcKYeEhGZoVPmqWFK7BC9gUyyMT/ayMNTAlcsu55TKNtyzSQ7er9JbiU2x4ZBiLAjk2TU6ylDBitvmACNNb3ScRTXLOXvumYS9YcLesFmVVVsKmcfN6qPFMfObsJEjZkRISGE88tTaGXZkqmQdQ44yoOc5rypLnSVHX3wPfWMBdMOc2eN3+Ql7w8SzcRRJwSJbKJQKKJKCbuhYZSvJQpK2SBuaYSbYzjRj7OBr/lznc3SMdiBJEgtqFnBm25mzmtp8JAFXgLcteRuBvs08MLCN8fQ4LouDefXLaatbTNBj7j+pa1RarDhnGGKsC9ZxSsMp/O+2/2X3yG4SuYSZNKyrWCQLHofHHN6xgKyb6zxNpCeo9lczkhwhlU+9rj1JrwmjBPltkHsOci+AngY5bK6mLBmQftysg+S7FmzzxFpAAiACFeEkUuGpoMJTcVz2nSvmeGLvE0ykJphXNa+cyGgYBgOxAf6y9S+c2XYmAVcAm9XGU3ufIpVPlYt7HbCxdyP39jzBkrol1C6qpdJnVg6dGNmDqqsEnAHcdjfxbByLJBNQZAJOH6rdQzKfwCfJZNQSo+lRBmIDOK1Owt6wOazhUMkUzOm386rmMbdqLi1hcyXh3cO7cdvcnNl2Jmva1uCwmiXYNV1jLDXG6vplnFU975jPS22gltZwHbu7f0tzwIvHdTqDiSjj6QSxXImFVa28b3kj8ysOWZdF8YH37WY14FKf2XulhLFK9SilJOoh1WkUI4Nd7UHS0lgJUWn1cE5DI6nwKBMlPxnlNBbXLuZPL/0Jt83N3pG9DJWGsFvsZEtZNEPDIltw2VzYrXbmV80vF6er9M5cvv/Frhf5+TM/ZzA2iMPqwDAM1rWv4+GdD/PRcz7KsoZXV37e7/Jz2YLz8dUsZmcmSp3dReCghRaTmoqBQbPVOWVxxAOG48Pc+dydjKZGqfRU4ra5iWViJAoJClqBXDGH2+FG13UMzLWPimoRXddx29zTVv8+6RkaZJ8xCzMWu8zijFihsBn0OMiVYG2F3POgTYD3KnCdNeM6SMJbi3gFCG8JvZO9DMeHaatsmzLboqAWmExPsqV/Cz0TPTSEGojn4owmR7lw4YVIkkRakuktZNlVzJNwBBhKDsPoPoYTwwTdQZbWL6VQMj9Y5kTm0BxqZiQxQq6YK0+9DrmD5Es5+mP9SEioRZWQK8SoOkpBLeCwmqXpbYqNbDFLNBPFZrER9oap8degaRrL6pextH4pXofXXLfHZq6WrMgKmqEd8fhjmRijyVF0QyfgClDlrTK/uSsWzmttxki56YwbqEYat82OPRBiZV0b581dSpM3DcW9YDukSqpkM7cdtD0MtFpLbM1nqCh37OjY1AFkPcsYYVyywgK7E9nShN9SiV/tB7edlsqz2Da4jXXt61hSt4SCWiBXyuGwOsgVczgsDhRFIewJkywkqfHXcMGCC2Ys3d832cfPnv4Z0WyU5Q3Ly4XaVF1l1+Aufrzux3z1nV8ldNCSBKl8ip6JHkaTo8iSTG2gluZw87QS9oda6Q2jWB30lfKkS0UUDDJqAYcks8wVoO4wi0A+vvtx9o7s5bSm09gxuINsNIvNasOhOUhradL5dPnaOm1OXHYXOjp2i52wJ4zTevhp/ScldRDyG0GLmQsXakkwMoAGkhO0EaAESrW5DEbuBZD94DzlBDdcONFEoCKQyWfY0LOBbDFLY6iRxXWLj7huyxvRgTVrDq7Wqmoq2/fnq/idflx2F/Oq5/FM+zOMJEfYObiTuuYzGJQtdOYSlCSJuoaldA9AzOqkLVBLIjnClr4tYIAsmXVGKjwVtIRbaB9tx1F04HV4yZVyhL1hssUskiShaRp+p59MMYOmazRWNGJVrPRO9qIZZi9JxBvBJtt4oesFuie7KWpF9o3uo7GikRUNK8q9T5lC5rA9UYVSgRe6XmDPyB7S+TSarpkJtLpObaCWgCvAvIDG6sYmljc1MJKMoeoaQZeHxkAEt90BqrQ/58cwZz8dxQXuAJ3FHP2lPFWKFSdZJC3NOAHiWDnTkqde2d/jIjtBckFxF17vIq5deS390X5i2Rh1wTp6Jntw2BzlPB6AxbWLCbgCXLTwoimLAx7s+c7nGYoPsaJhxZTZMRbZwqK6RewY2MFzXc9xxbIrADOQfWLvE4ynxrFb7OiGzua+zTSEGrhgwQVHLFNvl2VOc3pptNrZNN5FZ7SPTGoCt1rA4Q1jqV1EW6RtyntKVVWe63qOkDuE0+akJdzCWHqMZD6J0+ZE13UyxQyZQga/y49NseGxe4h4IgTcAeZXz8dlP4ap9SeDwh4o7jFzUrSJ/XWPCmZQIiUpfxzpaTASYKmG4k6wLwLZfsRdC29uIlB5C1NVlV+/+Gv+P3v/GSzZnd734Z+TO+d0c06TAzAABsBikDYn7orkUqRpuii7RC+pkqgXKsnlFypbVrn8QlWWKdqiWCvpL60oU6K8WoIbEHaRgcEMJoebc+jbOYeT/i/OTM/cmTvAYBcDbrifqlvAdPc9/etzzu3zPU/4Pv/p/f/Eem4d3dbxqT4O9x3mm89882drMutPiSAI3GmWn61m2ShtkAqmKDVKncd9mo+YN8ZCaZNSvUDT0CmmZwh6QvhVP81AiqreYLaSJWKZLGwvcLD3IAd7D6LICqIoMtU1hU/zsZxbJlPJUGvV+NT4pzjaf5SZ9AwvX3sZj+phJDZCQ28Q8oQ6tTCz6VnWCmtoisars69SapTQFM2J/tRybJQ2WMmt8IVDX0ASJbyql+HY8F2f2bZt3ph7g/dX3icZSBL3xbmyeYXV/CrVdpVys8y+7n28vjDPrLbJ8/uGODGwW/pId8TEfYrXI24/v2YZfLeSY93UkawWmu1BExUekVv8DU9r56bEAJglsBuMJEb43Sd+lxcuv8BAa4DR+CjblW0M20ASJXpCPfRF+phITvDUxFP3XMOljUv4NN+uLbyyKCNJEtOb03zx0Bcp1Aq8fO1l6u0648lxBASK9SKbpU1evPoiV9av8I0T32AsMXZPczkRWFu7yNz8m4iCyIA3CorGUm6JxewiT4w+wbGBYx2x0tAb1Fv1TlQk5o9xqOcQZ/QzZCoZAu4ANjZBd7CTXuwN9tIwGhzqOfTz+bfZvg5G3jmX7DZYbUekwA2PovYNd2kNzOoNTyT3jbRQ8oO2vMcvOHtC5ZeY//vV/5s/feNPAegOdzsusNU8r828xnphnf/jV/8Pprp/8gm2P0skA0ksy+r4VoATZWkbbbKVLHPbc/SEe3CrbmxsbNsmr7fYWr2AYBnU2nWaehNZlEFWON53GENW6W7XqBfXGYmNIIoi51bOEfFGGI2P0h3qRpEcL4xDPYf4/MHP41JddIe6WcmvoIgKgiiwVdxiNb+KJmu4VTcRT4TDfYfxqB7OLp/l8dHHaepNp1PJsvCoHhazi3z3/Hd5ZuoZHh99fIfT7E3S5TTXN6/TG+rF53JE02J2ka5QF4qksF5Yx7IsxlKHWVxf5c35s3z18NM7ZwTZJlhlcN2Hl81tPOUNc0Tzc7pZptRq4WttMu6KMSgLiHfpHcPxwblhcn+k/wh+t5+rG1dZK6yRrWZptB0xN54cZ7JrkqHY0AemZHbMaNoFEbEjXOcz82SrWSZTk51/z6RnOsf74tpFdEvn8ZHHeXryabza3R0p6XKa95beI+wJ7zB1EwSBbDXLazOv0Rvp7Rwnt+Im5AmxWd4kRQqAvkgfbsXNe0vvka/l8agekoEkMV8Mn8uHickTY0/wGyd+g4A7cNca7kVLb1GsF3Gr7o/0ex8rtglGxjnOdhvQbz0n3EiN2S2wDSeiIsccIWNk/lqWu8fPFntC5ZeUpcwSf3HuL1AkhcHYYOdLPRlMEvaGub51nX//7r/nf/nq//ILkQYaiA7QE+7pDIyTRIlCvcBafo16u45bc2oAlnPLnfTIit4iJKlEg0kQBNyKm2KjiGAL2LZFNNxHoFlheeU8uUqOofgQgigwvTXNQmaBkcQIfeE+vnToSxzqPdS5G2+0G6wX1kmX0yQDSYKeILVWDQGBuC/OcGyY5/c9z5+8/if0hnsJuoME3UE02TFCqzQrBNwBio0iR/uP7rhTv52N4gYNveFc5G4Mu9Nu2PjX23XKjTIvXn2RseQYAcnLpY2rnOgfpC8y4kRPrAYYa067tzp2751rm85dr22BFOxceIKyzFPeENsyZM3rFMw2luAjJRr4xNuiW2YWXEedu2ec6NdoYpTh2DCVZgUbG5/m+0h28VOpKc6vnHfGG9wRVTEsg7bZZjzhRI+Wc8udup+N4gZXN6+iSRouxUWpXqLWqpGv5nlv+T1UWeX5fc/ftb8XsgvU23X6In0AbJe3WcwukqvmMG2TQq2AS3XxW4/+Fn6XH1mWeWLsCb715rdotpu4VEd0xfwxTk2c4s25N2nqTU5NnHKOvaIxmZrk2alndxVKu1Gql/jBlR/w2uxrFOtFVEnlSO8Rnj/w/F0DKR84dtNpORZcYGwBKo5YsQELbg5ZEESwLCeqIgB2w6lT2eOXmj2h8kvKq7OvkqlkGI4N3/Wlq8oqQXeQdxfeJVvN/myPkb9PXIqLZyaf4ZXrrzCfmUfAERSb5U2nADY22GmHvmmR3zJaKKob0zKpNauYloFf8zkioV7EZbSY3XbSNKcmTjHZNclU1xSNdoPrW9cJuZ0W1njg1v7L1/K8Ov0qiqTQNJpc3biKS3ER8oYwTAMzZ/I7j/+OI4pqxR01GCFPiIA7QKaSIVPOsJhb5EfTP8KjehyxccfdsmEZnWOrmzr1Vh2P6qFYL7KUWyJXzeFRPeimzmpdoFBtcXrlGn3+G74fguJ0YXieAGmXNljbdnwwWpfA3MYp1AmCug9cB6hYIu83q6RNA0EcQdTn0S2LoOjmkNKmR2xg6auYtgdRmbhraKAoine1Ht8cTggQ8Ubu2Zr8+OjjvHL9FWa3ZxlLjHXEimmZTG9N0xvp5dGRR298DMe4z7Zt57jrLQq1gtMiLUrU2s75oFs6r5uvc7T/6F1/E4VqoVM4vVnc5Pzqedpmm5AnhCIqNNoN3lt8j4gnwqPDjxLzxXh26lnOr5zn/ZX3ifvjRH1R2kabjeIGfeE+vnzkyxztP4ogCMR8MeL++H3fNJTqJf7ZS/+M9xbfI+QJEXQHaRktXrz+IhfXLvJ7z/wexweO39e2PhYEBaSEUyjbmrlh7KYCNSeSgoAjVlwgCk5xrd0GeXzPBG6PPaHyy0qx7jiSqvfoSPCqXiqtCvVmHX7OrBruRSKQ4CtHvsJSbolzK+eIeqNoskYymNzh2aIpGrVWjbDqYbj/KJrhWLiLgkgykESWJHK1EoVKlu31SwzGBumP9Hd+3626Odx3mJmtGTZKGzuEyuX1y7y79C6yKDMUHaLRbpCpZshWsgTdQWdukD+GS3E6XNpGu3MBtGyL5dwy2+VtdFNHEiVaeotXrr/CdHqaT+/79A5/EJ/mw7ZtLNtCFEREUaSpN1krrNE22nhVLzF/rHMhq7TqXMqFecx4mO5QzBEdcte9vSya56DxGqCAFANEJ7JSfxnTyHFePMaWYdIlq8juEZAs7PYSWaPMe22dUuM6hXKRtcYApvwqk6lJJlITu0YMWnqLM8tnuLZ5jVLdqSfyuXzE/XEGIgO4FBdRX5S4L85yfpnZ9CxdwS7emnuLzeImMX8MWZJptBv0hHr4W0/8LRIBp625L9LHUm6JltEiX8tTrBdptBuEPWGn8NkyGYgOIEsyVzaucGX9CqcmT+1Yn6Zo6IaOburMpGewbIuuYFfneUEQqLfr/PNX/jn/9q1/S1eoi2MDx/ji4S8yGBvk7bm3WcmtIEkS48lxPnvgsx9Yg/NhfP/y9zmzdIbJ1OSOotuuYBdXNq7w7Xe/zYGuA2jqJ1SkKqigTYCZBmUI2k2wK06ERXA7KR/kG6LEdFqS5QS4jt9IC+7xy8yeUPklJeQJISDQNtqdsPPtVNtVAq4AHtfPWWfBh+BW3Ux1TaGbOhvFDURB5Orm1R027IV6AcuyiLsD9Ae6EFQXCX+C5dwSpUYRWdYoKwrtrWlUvcnxsU/d1YEhCiI+l4+57TkO9x3uPH564TSVZoV9Xfs6tSCDsUEsyyJTzdDUm6RLaQ5MHaA/0s9idpGAO4AgCOSqObZKWwTcAbLVLF3BLg72HsStupnbnuPNuTf50uEvdaIHA9EBot4om8VNesI9JP1Jzq6cpdQo4Zbd6LZO1Ot0C1VbVZL+JIoaYK4E3cl9VJtVVrPTtHSnfbov0ndLRJh5aL4HQmCnuZ6YAitApjnLltxDSu1CFgRAAG0CQenF39zkzcwCl3MhkmY3muoMU3zp2kssZBf49L5P74gOWZbF67Ovc271HHFfnNHkKM12k/cW3+N7l75H1BdlPDmOR/HQNJpYttPCOxwfxqN5uLpxlWa7yVB0iBPDJzg5cpLu24Y1jsRHuLR2ifXiOrVWjVKjRMwXQxCcolq/y0/QE0SRFJZySyzllu46r4ZiQ1xcv8hWcYtio7gj4lJulLm8dhnDMtAUjYbRoG22eeHiC5xbOcf/eOp/5GvHvsZWaQtVVhmMDiJJd1+cbdsmXU5TqBcc92ZfbFfjuqbe5LXZ1wi4A3edl4IgMBwbZiGzwLnVc52o0ieC0Q8FBSwXSAPgsqE156R3BLfzIwWd6J3ruGMCp/1i1Mjt8dPxQIXKP/2n/5S/+Iu/4Pr167jdbk6ePMn//r//70xMTHRe02w2+ft//+/zZ3/2Z7RaLT7zmc/wL/7FvyCZ3KvyfpA8NfYU/+atf8NGaYOh2NCOkHLbaFNpVpw7dN9P5+D5s4ooOLbso4lRvJqXlfwKxXoRgK5AF81kE0mQGLR0tmyVmjdKl8tPpVkhX8shZRZImS1EXxRJknathZBECePG8D1wLiCb5U08imdHwaogCEiSRMQbYTY9S7lZRlM0Pr3/0/yr1/4Vy7ll4v54ZxZPsV50fFV6DnWEQ1+4j+X8MpslR5QAeDVvJwUytz0HwHphnfXCOh7NQ8QbIVfLOTUbRpuprimCniAbxQ3Or5zn7PLZzkXRxibijXBi6AT7u/cj6KtglUCZ3GXneihRw2hvIyndsKPDx8t8sUW62KYndIiU6ZiWhT1hEv4Ec9tznFk6wzNTz3R+Zb24zpWNK/RH+jupqovrF8nVcgzGBqk0K4S9YSrNCq/NvMZ4cpzHxx5HkRQSgQT7u/ezkFlgKD7EVw5/5a7jFPPHeGriKV6dfpV8PU+hVgCg3CwjCiKaonWGMwZdziDKO493f6Sf8cQ4r868Sr1dd6Y+10udtedreYZiQ4Q8IWRRJhVM0Rfu48rGFf79u/+e/+1r/9uuhestvcVSbomLqxc5t3qOervuCCdXEK/Ly3hynJMjJztRN3A8c0qN0j2dnT2ap+Ny+4nQKMHb34Xp18FIQyQHqQIE/RAZAqHoRFLkbqfrRw450TzPY6AMfjJr3ONnmgcqVF599VW++c1v8vDDD2MYBv/oH/0jPv3pT3P16lW8XucL9u/9vb/HCy+8wJ//+Z8TDAb5/d//fb72ta/x5ptvPsil/dIzGB/k68e+zp++8adOmDzUhUtxka/lSZfTjMXH+M1Hf/MXopB2N5KBJG7VTbVVpTvUTSqQomW0ANBkjbbRptqu4jIajIkiZVGiIYhsN6tsLZ8lqNfxBVJcy83SNtsMRAbY170PRbqVT7/pbHuTeruOV/UiiVInHXM7kiihm3qna+TU+CnK9TI/uPoD5jPzrBXWkESJkDvEkZEjHBs81vldt+pGN3XKzTI93JpkPJ4ax6N6eGvhLb5/+fsICHhdXsKeMKqkMrc9R9Ad5OToScaSY2yWNkmX02yVt/BpzvA9URCxbIvt8javXHsFVVIZD9RwQvV3nx8X1xf5TwuXOVt8DY8YYjA5zuHBhxlOjtPUm2wUNwi4/EiiDOYtd1VZkkkGk8xtz3F84Hin/mQlv4JpmXhUJzqQqWTYKm2RCqaQJZlaq8ZmcZOW2SIZSFJsFMlUMp2oiSRK9EX6WM2t7hBytzOWHCPqi1KoF/hW+lvkqjlHELiD6KbOdHoagH1d+3a1rVdkhacnnyZfy7OYXeTC6gW2K84Mm7beJuAO0DbanUnVkighiiIj8REnurFyjkeGH9mxzUqzwsvXXubCygUub1ym3CwTdAUJeUP4XX58qo/3V97HMA2e2/dcR/y6FBeKqNDSW3etE27VLn2Ykd3HQqMAP/znMHsaPGEI9oGZgI1l2M5Dtwpjz4FYdbx65BR4Tjkmb3L/fbfE7/GLzQMVKt///vd3/Ptf/+t/TSKR4OzZs3zqU5+iVCrxp3/6p3z729/mmWecO6hvfetbTE1N8c477/Doo59gWPKXkN879Xv43X7+05n/xGp+FcMy8Gk+np54mm8+881Ou+bPCrlqjqXsEoV6AVVW6Qv30Rfp+0jdIDeJ+WKMJ8d5f+V9BqIDeFQP7huFs+vFdYbjw0R9UVbzq7gUF363n3Jxk3MLbxP2hPnU5DP4NB+6qVOsF5nfnkeRFPZ17+us1aW4GE3ccm1VJZVkIEm2mmWrtEUikHDanXEuHhvFDSLeCAPRAcC5+H312Fc5NnCM04uneeHSCwS0AAd6D9yolbn1uW3bBhukXfL5PeEevKqXieQEx/qPcWb5DJqkgaUjCgLllhMBEASBUr2ETYuQKpD0uMGqgBhAFERSwRQruRUurF1gZN8gErfccHXTYLtS4sWZ87xw5T0qto4VGaNhtnhv5lWurrzPp4/8CiPd+2noTWRPGP9t0aabBN1BspVsZ+4POF1St3/W7fI2giA44qmyTbqcptwoI0syUV+Ueru+Q6iAc/G+eax2EyrgFOce7T/KXwb+ElEQnQu/4KSebu7XWrtGf7h/V38WSZQ42n+U95be48LqBXrDvUQ9Ud5svommaLgVN9lqFt3UaRttZFV2ohuW0RE1tx/PV6df5aWrL7FdyzFf2UbzRqhLChXbpppbxufyMxAZ4NrWNfZ17+t0HIW9YQ73Hebl6y/TFey662Zjs7hJxBvhYO/BXffDx4Ztw/Xvw/J5CI2C9zaBZ4WhuQqXZYgOw/CYE1FR+vaKZ/e4i0+0RqVUcorgIhHnjvHs2bPous5zzz3Xec3k5CT9/f28/fbbuwqVVqtFq3XrTqFcLj/gVf/iIooiv/3Yb/Orx36VMytnqLccZ9rJrsmfuUjKpbVLvD3/NqVmCU3WMEyDs8tnGUuM3dPb4oMQBIHHRx/Hsi2mt6ZpGS3HFK7VItaEJ5U+BvQgS5qLy3KFrN5gs7xJf7SfRwYf6dRQ7O/Zz8XVi2xXtrmyfgWf5qNttlEkpVML0bBMNow265ZBI9SDXdrCYxmkK9tg2SA4U4w1SePYwDGGYkOddUqixFB8iKH4EBFvhEvrl3a90BbqBQLuwK5+KrlqjpXcCv2Rftyqm3x2mu3t6/SrEi5RoipKZLevckUAv1Sk0Vgh7vFAfclpE5VSoE2C6OmkoLYaU1h2lK2GzmK5wcb2MhubM7x86XVkSWQ0HEBwxzBcCVKhbjbzK7x88bsEvBF02YVq6oR2sf3XTd0xZLstNeZ3+am3nLECNjYNo+F0VlWuU2vWqLSc1E+1WSVXy6F4IhTrRQq1An5RZlxz4bsxL2Y3gXET0zLJVhw/lXKzjCIqtI0WcrNJoG7RaFZoVpeITdbBMOC2qd5L2SVen3ud7dI2+XqeXC3nFDJbVqcWzDTNTupnJb+CKqk09SbblW3ytTyGaXQEWbqU5qWrL5GtF8mICq7UFF5ZQ2/VyDbKlN0B/PUSnw12Y5gGa4W1jlAB+PT+T3Nx7SJXNq4wEhvBrTkifLO0SaFe4FeP/+qu58rHRblRZnX9LPLlv8QuVwj5DWK2iXxTSIsqyGEw6zDthQNPPLC17PHzzycmVCzL4u/+3b/L448/zoEDBwDY2tpCVVVCodCO1yaTSba2tnbdzj/9p/+Uf/yP//GDXu4vFW7NzZNjT37i71ttVqm1ayiS0umw2I3l3DKvzbyGS3HtiPK09BbXtq7d09viw3Crbp6beo4DPQfYLG1ibqfxn7tMf9HAq+WBPKO6znAqReHEI+imTsAd2FHomQqkcA+7WcuvcX7tPLV2jaN9R5lITdAX6aNsmZxpVMiYOi5BIJEaZz63TKVRorf7IGHbwrQMWnoLn+bj1MSpe3Zi7evex0J2gdX8Kt2h7s7FvNQosV3Z5uTIyV3bdZt6k6bRxKN5CNTWeE6pcUYVWGk1MCwb0WrRbNWI+W2OTyR5bVlGVnpAkh0vFX3J8cFwHUeWZJrAGdNDS9zPRv4CC7kiohIm4x9CDC2SsrKkazre7VX8XUGqiotApJ+FtUv8ePYN/N4wUnYRd2oM7pj0nKlkSAQSnWJUwzQoN8pc27zGpfVLjtCq5pnPzJMIJPC5fJ1OmfXyNu9tXUev5ukKpMg3qgiCwPlmhQNAn8v3gRfnWqtGsVHk4aGHWcgssFXYQM2VkIolGph4FS+JtoDr1TegLMOpU6BppMtpXrr2Ek29yUhipDMFudqoslZco2k2qTarhD1hJFOiUCuQq+XoDnZ3RigsZZd45forneM/m5llOb+MLzUB7SZao4giCCiygiYGyVW2WQl0sW3byJJ814DCfd37+L1Tv8e3T3+b+ey8UytlO5HEXz3+q/z6w7/+oX8fPymz6Vlen30dMzfNeHmVgtGEwgxdrhAHA4N4pBudRqIX5BJU8w9sLXv8YvCJCZVvfvObXL58mTfeeOOn2s4//If/kD/8wz/s/LtcLtPX1/cBv7HHzxqleomzy2d5f+V9Z7aJ4mZf1z6ODRzbcVd4k+ub19FNnf5o/47HNUWjJ9TD3PYcR/qOdNpNPwqi6Ayf61aC8MZ1qKswPg43uy7MFmL6Au73rxNJmNjyAAY+ZG7dmQfdQYI9QWRR5ouHvshktyOmTNvmQrNK1tTplVVEQSAR6iF44DOcXXyXtcK6U3Miq3SHunlo4CEmUhN3rdG0TAQEesI9PDv1LG/NvcXc9hyiIGJaJj6Xj0eGHuHE4IldP+PNYYdmI0+4dB1B9fDw4MMMNioUWo7jrqbn+VK4iuw5yvsui3KrSdjjuzGLpwuMTTC3KLV81PwpSpJKUujhauEMUVoExTaL9Syu7gMolQ3CbcjXS0QrW0QCXaxW0lSL6yxOV3kotZ+V9atUFy9ybOoJkpHuThpHN3SO9B1BlmQsy+LNuTc5v3qe7lA3W+UtJzphtsnXnItbxBch7nVETVpy0ZJU7FaViKSQkFUM22K7WeGVSprfnXi6U/+zG4IgINww9nto8CEypXfJlDcxI30EXH4SWoitVgEh0g0XL0IoBI88wvXN6xTrxc6xUyQFn+ZjJD7CWn6NS+uXnKGVegPd1CnUCgQ8AYrNIrYtcGz0JMPJKMtbLzCtbXGw/wnSxQ2ato1b9eFtNSiZBtzIiEiihCKIVCvb5CUFydQJuu8WqA8PPczB3oO8v/w+2VoWl+ziUO8hUsHUB/9R/BRslbb40fUfYdomo4lhooUZ3PkyuupnvZlHQOSh0M3BoCa02tBz72Oyxx7wCQmV3//93+cv//Ivee211+jt7e08nkqlaLfbFIvFHVGVdDpNKrX7H5OmaWja3oCqn1fKjTL/7p1/x9sLb2NjI98Iyc9szXB14yrfOPENhuK3Uh83fT/udYHxu/xsFDfIVrM/kVDpsLgIGxuOSLmZHpCKWN5L1IJpGtub9Ih+0koGXRmiZE0RMpWOSVm1WXU6aW6bxps1dbbNNklZQbwt2hMPdvHpQ1/man6NiG1wwhclFUztiKRYlsVCdoHprWkylQyCKBGPDdEbH+aJI1+hVtpEb9eRJZnuYPeubao3ifli9IR7qK6+hWJXqXl6kASBlDdIyhtks7hJd7SHiLqJYOiMxbs5vTKD3+VGFiXHx0LQ0JsrrDZ78KQm6dO8bBfXqVleugODQBOVKkgCRsCHll9CkzUKlQyiZVHLr+Bu1jlihHnaNtjKaFytXuPi3BzDo0cxh4YI+6M8Pfl054K/Vd7i4tpFesI9jKfG2ShusJxdZrWwStAdJFfLYdkWpmmyVN5kVW+hSRqyR6NUWEeSZCzLxqOoKMlJysnd5hjdwqf56An1MJ+ZZzjQQ3e+SXd8AoKOCMi3KwRlDwl/AswaXL2Kvm+SxezijvMz7o+zmFvEsAzqer1Tu7JWWGO9sI5pm87ohkaF/SMP8/CQjF8+jd9bZiO/xpB/nYBdwqWJNC2DgOqhUs+jG22UG+eIaVt4TJOCqTPkiXbqmu7Epbg4OXryAz/3x8n01jSVVoXx5Di62UQPJVEzZWi0iLsDpFsFCnqNqOqHRhasAEwd+cTWt8fPJw9UqNi2zR/8wR/wX/7Lf+HHP/4xQ0NDO54/fvw4iqLw8ssv8/Wvfx2A6elpVlZWeOyxxx7k0vb4a+Llay/zyvVXCHvDxHwxFElBN3XytTwX1i7gd/v520/97U6u3rZtZybLB2R1LMuiVC9RbpQ7VugfmY0N0LRbIkVogPsCZSHDZiOIq2nhLfuoeSSS0jy5dp3r5W4izTqyKFFtVXl48OEd/hlVy8S0Qd3FME0URfrD3dhAly+McttrLMvirfm3OLt81tkHqoeVWpHM9jwBb5SDE08xGOplQnMzqLg+9POKosjxgeOc23iLTKmApCXQZLVjcOZW3QxHgwh6EVoVjvceJ1+vMpvZIOjy4lIUGs0qlXaJ7uGn0EK9aKKIbuoICAiiBmjEA32sbl7H8MVRBRFZlGkZLTZLm0i2wPB2nSmlBZpOqn+cqDHIhfXLdC1neXTwFLGHv7Cj1mg5t0zLaHW6bHrDvXQFu2jqTSzTaQ/2aB68mpeK0UZEwufy4HZH6Oo9RCo1gSBI+ANJGq4AS5ZF1TTw3aP4WhAE9nXvYzG7SDq9RKJWRYg74rdqNMi0yzwaniCgeCCswvIyViGPZVk7in0T/gTJQJL57Xly1Ry+GymnpD+OoReJeSIE/VHKtsiUP8+IalK2otSkCAuVDKGml6g7S0Krs9oq4Vf8RHxR8tUc7RtdUs12g2jEg2nqPDJ0grA3/IHnwCeBZTmGhGGPsxZTclGJjBGNb2FvFEHX0KUmpVqeaDkDzTrs+xoM3z1Qc489bueBCpVvfvObfPvb3+Y73/kOfr+/U3cSDAZxu90Eg0F+93d/lz/8wz8kEokQCAT4gz/4Ax577LG9jp9fQBqtBq9Mv4IqqztcOxVJIRlIohs651fOs5ZfYzA+CDh3hKlAivnMfOcL8CamZTK7Pcu1rWtYtsXF9Yv0hHrY371/R1TmvrCsna2QyhYtKUO6FkVBwC3JiFqAWDDEO/Pn2Mq9xILeh9wSQa/RG+rlydEndwzD+zC5ZN94lXD7K+t15s+9ypn3/oK45IV4F9NxD5ovxlQgQTq/wtbSGZIH45xtGIDA0C6GfXfSF+lDGXmC7etbLNcLGKaBIjv7fTQ+StRVg+xlEGX8Lg+fnTzGbCbFte1V6u0WIZfEydHHkHse59KNeoibEaCm3qRQy1NpVjFti+XCCtFyGlWUEQXHDVfJZBltKEQnpsDrRCgURWGkfz/Vaon44iaeSgNuEyrVZhVN3hk9lUQJr+al2CiS8qcIeoKMJka52qxiGjpuo0W5UcSbnEBL7UMQHM8c0TQxbRvD3jlB+ybZijNJu9QoEfaG2S7MUdCzyHULUxRwiQrHgsOcCN+YeXTjfFFljZg/xlJuqSMWFFnhcO9hcpUci7lFBATK9RwpV4GxiExvOE5dlIjqReTGCpXmcUxVQxEsvKJEzoSRyAEmw8uUmk1KuoSA44tTapTR9SYBd4CJ4Uc51X+Uo4mRDz3+nwQ2NhbWjsfK/hGEQZ2AeglvdpNIJY9alyA4Csf+Bpz44o6i5D322I0Heob88R//MQCnTp3a8fi3vvUtfud3fgeAf/bP/hmiKPL1r399h+HbHr94bJQ2nGJJ/+4pmpg/xnxmns3yZkeoCILAVNcU85l5CrVC52JgWibnV85zevE0PeGeTm3LfGae5dwypyZOsb9n//0vrrsbLl26MRBNBGWLhqGg2+ARBWi3MaMRdF0gV7WJuD0QDNK2+zjqi6HICufXzjsGYzfeNyTJqIJIwzJxi3e3DZdtkwnFc8O5Fcjn4aWXmL76fUS7RMCtMGNu0KqXiEYTkEqRDPeQzq9hlLfRQj3Mtev0KOquUZs7SfUcI1FboE9wo4syiqQSdAWcThhDBUsFnyMMPKrG4Z4hDnYNYBhVZGsLMfgZMqIX2dRpWRYxXxxFVrm8fpm22catuOhLjrO6+B7z6Vls2yYRSNA225yyg+zr7sHt3VlLoYgyDY8bo1KG5WWI3TYCwOXreNvcjmmbaIpGtV0lHowjSzIBRUMwddqqG090gJbqpmlb2DbUMKlaJsOqC98d+0k3dN5ZfId35t9hLjNHvpbHtEzckouDLhcHDR+x7iF6XFGSWuiW900uB9EoQjTKpDjJfGaeSrPSif54NS/7uvexlF2iN9LLw90efIKAKLrJGyaWrOGTGoiIKFYGrCCVlohP8+N3+zEVN08PTbIxv8WKqeGV/Yg2JDxhsG1GBh9mcuRxHvZHfmY69CRRoifYw+WNy53Ioi1KFMP7qXn7kLo2yGSWGRt/FsafhnsY0u2xx5088NTPh+FyufijP/oj/uiP/uhBLmWPnwFs7JthhF25aSwm3BGLGI4Pc3LkJKeXTrNd2catujm/cp53Ft7BrTpTjy+sXmA8Oc5QbIh0Oc07C+/QF+m7/7H2g4OQSMDKCgwMACaGLSLaNkomixUIUIuFmZ69SMQbpNfrJih101YmGHD7AMf19eLaRcaT4yiyQliU6VFUFtpNkoKAduMiZ9k2OdPAI4j0KTciBqYJr7+OvrxIJuolIAZoagGKPhVvrQFrq+B2IYdCWLZFrVkmJQ2wZbTJmwape3QK7cDXjRjdRzhzHlzdoAacKJJeh8omRB8Dvw3tBWd2jyAhmkVUauA5CsogUSS6ZJVVvUWXrKJJGvl6npA7iOgKEJQUYuFulrv3UW/VOdDtzJM5sWXhb9/djlwx6nglDbfmgWp1x3MD0QHOLp+l3CjfdRy7gl1sljZp6S2ylSyqbWK3W4g9B/ErbiS9ietGGq9uGui2TUJWEe5oTz6zfIaXr77MRmkDwzToDfciizLpcprTQhpfWedAbBTZLVE26gRlL0K5DJUKnDgBmsZoYtRJra2cI1PJ4Hf5nXRmPU9XqIsTAwfo0eZB6GMgWmVtdc4p3NVbTCZ9iKKC2VynqUfp7R7BJam0bIsj3QOorjDfy3UxXVjHtC2CwSQ9qUkmU1Mc9ATv77h/gkykJphJz5CtZne4WjdlL/O6yODI0yQPfsnpKNtjj/tk72zZ4xMj4o3QE+5he3uFeFAGWaHtvvVFm66kiXgiO3xEwImqPDT4EL3hXuYz8/yXc/+Fd+bfoWW2CHlCbJe32SptMbM9w6mJUwzFhphNz7KSX+FAz4H7W1wgAE8/DT/6EUxPQ1cb1ZdGy7ixwmHqB/ZRsEyK9QrJQBjsDC08aOItURXzx9gobpCr5UgFUwiCwCHNh23DmtFCt20kwASCosRBl4+ofKOVY3MTlpYQBwaRMlvotoElgAVILhfUG85dfCiEbdtIooQkCNg43UX3hSBC76ccD4vCNNQdS34kDSKT0PMEiHloXQFjA2wLpBBoJ52ZK4LT63TY5cOwbZaaZYqixGDfUapGi0ajhJZfwmU0eWbyGSK+CKZh4nV5Sadfx9eyd0jQtmVQ1GscD46i1E3w7JxLkwqkONR7iNOLp6m360S8TvSgZbRo6k2eGHuCmDdGvp5HFEQeC/Wx4I1QLqzRFGXKpkHLdlIRo6qblKSSMfXOxb3cKHN5/TLFRhHTMukOdXeiEwPRAZYsi++3F1iZ/y79+JBllT7Rz+HgEL2PPg0HHcM0URR5YvQJekI9zKRn2K5s41JcfOnQl1jOLbOYPkOTMi73AANhD+lmjXfW5wkJOiFNIF1uoEgWA8nDpCJ9lLHxCRICVfalDjM+9DRLtTxZo4Wm+QgoGklZxbdLlO6vm75IH4+PPs7b828zk57Bq3oxLZN6u05fpI+nxp/6iQwa9/jlZu+M2eMTI4LG5+pRZs+8icdYwe2SqXV5WO8JsOqSKdUafOHQl0kG7/a6EASBrlAXi9lFVnOrhL1hx0bc5UwIbupNctUcb829RdwXRxREqs3qLqv4APr74atfvdEBdB7VU6PVE6aWGEPx+rBKeWzbRrWL6JKfuhin5zbLfElwrPEt+1ae3iWKPOT2M2S6yBo6JjY+USIpq3huv9Dk82AYSG4PI74Ub+evE9KCKBa0BQHFpUGlSq1WxKV5iASSNC0LRRBwf4CJ2V20TGc4nOIDTwvCYXBHwJO8UaMTcqbbWhXAAtHnTLK9Da8o8agngK9Z5nqjQJ8/hRsLSdXQfGE8igdN0Tr+ICe6T3Cpb4OZs28QrSlomoeK0aBs1Nnn72MfIXDXnf1/G6IocnLkJH6XnyvrV1grrGFjO/VNNhzsOdhJtdjAVcWD1qywrnpx+WIIAsQlhQnVwwHNy5apU7ot+pQup9kobdBoNzoi6CZto02hUWRLahPq6uZg5BCirjNjVllLuHhoJE7QbIMJflEiLIjOzCZRQhIlXLILj+rh1MQpXEKBubWrmNUNp8ZGUjjUO0p3wE9/aB2/3MLriUJoDF0UMS2DuFAHZNDGkCWJ0UCcUX72EQSBI/1HSAaSLGQX2CptoUgKw/FhhmJDH9mYcY89YE+o7PFJUdiC//rnnJxewRcIcrmxRENv4b9ikFiQ2J7y8/jBI3z10OP33IRlWbw68yptq00ikKDWqgHOl6NbdWPajvPmenEdAeGexmkfSDAIR47A4cNojYMEqz8mrW9g6SFCaougXCHXdNHyHySs+AiIEgVTp25Z5KpZ3Iobn2tnmkISBBKySuKD1iMIjuU4MOHtZbq6znp9m4icYtXjwiVAy2iQLW8x0X+MgCfCpmnQo6iExA/5M263YX4eXnoJZmedOhyfD9xu6O2F556DgdtiHYIA0genzFRBpFdx0d2qklDLzlA8zbfjNbqpI4kS3aFu+k/9N1ytwNzsWSpeN75AhEciI0y23Li2c/DII07q7Q5kSeZo/1H2de1znGltm3KzzAsXX+DFqy8S88UYjg0T8UUp6TqC0eJzvQcYS45hC6DeJuIEc2eBs2k5bcKGZdw19yZTyVBulIl4I8heH/bwIAFPGNUyeXtrhuszr/LQgS847duWTj67SHXtIoqp43V5KdVLLGQXGI4Nc2rsWQ6Gs6yX6ui4CXt8qIEQOVHAY2VJtd8CLLbNMg2zRr/QICy5wfUoyP1Ytk3rRkrU9VFE6V8jXaEuukJdH/7CPfa4D/aEyh4PFsuC1R/Dj/5/cOUqnniQI94c3U2FWSNCJqUynG3yqDJAz+ExouIFsAZA9Ny1qVKjxHJumYgngiIrFOvFHVNsvaqXXDXHcnaZfT377jnT5XaK9SLLuWVqrRouxUVPuIeEP+HUELiPkZJj2PVLFJrL1GU37uSjXF7PMC4n6ZY15vUGBcOgZbTZLm4wNfQI50yDI6ZB8KOEuGMxcLmgViPmDfB8/Aiv566yUlqn3pDYaDYIhOMM9R1mYOA466ZORJKZ0jw7PFruolqFV16Bv/xLp1jV64V6HRoNJ9UiSfDuu/C1rzlOq677H1QXveH/slXa2tXHY7u8TSqYIu6PI4kSn/q1v8OJd97AmJnGVW0gtwWIeNCfPMJqf5T6xhVUSXVmE91x560pGslAkncX3+Xs8ll0SyfqjbJaWGV2e5aEP8HAxDMkuiaZjA3eNfBRty0kIHDbMQm6g7gVN6Zpohs6yo003M3JwqIgokhKZ8ifadss6E1sb5R2eQN3+RIpn8FmZYPtWoVgLMCwHUPA09nOXGYOj+bhM0OPMRA8DfIQiC5sbLKmTqbtJi+NUpX6cQs2/ZJE3DWOpE1iygOs6i2W9AYVy0REICErDCou4j9jtSl77PEg2RMqezw4bAtm/l+49v/C1ipEXeCq4JIr9LtCdBlumqEh5PEg7kwZGj2gr4O+7NRE3IFlO6LEtm3CnjAhT4hCvYDP5eu0sTb1JqVmiQPdB+7ZXQROoffFtYucXjxNsV5EEiVM25nQe6D7ACdHTqLICpI6QK86QMIyKJoGyWCLHuVV5tPTnLYsmqKCZunYpsGxnoMcHDhG2mzzfqPCY57gjjvglt5iJb9CvubUVMR8MXrDvc4FMpWCkRG4fBmGhuhzx/l610lWGxky22tsezVaj30aV/cQiiAyrGj0K64PFkO2DX/1V/Dd78LSkhMpyWahVgNVhULBSbesr8P3vuf8znPP7d4uapqO10w262w3GkXq6eFI3xG+X/o+G8UNkoGksx8tk3Q5jWVbHOk7cmtuj9uN6+nn4aFHnfcWRRbtCm+tv0/68tud4vuwO8yxgWMc6j20YzbPdHqatxfedkTJDWHU1Jvka3lW86tM+WLIkT4KlklEFDqpHNO22TJ0umSV+G2pumQgyYHuA8ykZ8jVc6QCjsmkaZnUmjXq7TqGZRBpRchWs6g25C2ThKqxrq/RqspY7kHK1TxxoY6uKUjGJpIxjE4AWZLpCfWwmFkk2/8F4loTWtdAsBHQiNtNIrJG3fNFDNdDeEUJVQAEGdO2udSsMaPXURHwiRImNkvtFptGm6Mu/61C7D32+AVnT6js8eDIXoL5vwRcYMXB7QI7C2ggt1BsEaVdhnAKtorQ0AEZ9LVdhYpP8zEYHeS9pffoEroYig2xVlijWC9SbVZpGk0kUeL5qec5OXLyA9s25zPzvDr9Kl7Ny0RqovPacqPMe0vv4VJcPDL8SOf1qiiTEGVQXHQf+CxvRQf5q9Xz+PUmfpef/sQo3dFBFFnFbdtsGG22jDaDNzxOtkpb/Hj6x2wUN5wL8g1/j4HoAE9PPO20XT/5pDPsbm4ObBtNlhlttRgND8ATT2BMTNC2LWRBuK92ZKan4YUXHHGh647AWF11/l+WnYjK6qqTBlpedmzhJybgpjGjbYOZcdJ2r52F1SIY5o3UkAT9/Yw+9RTPTj3L6cXTLGQWOp1dMV+MJ8ee3DE9uoPfD34/q/lVfnjpNQzTYDA6iCIpWLZFppLhx9M/RhREprqmOhOHzyydQZO1HXbxLsVFd6gbj+ohk53nZO8hlkWRVaONJgiYto0JpGSVIy4f0m3nhCiKPDn+JMv5ZV6dfpVqs0rUGyVXy7FSXMGyLFLBFJZtcW7lHJY/gTc2SEiuItolFDVF2/ZT1UFTwrRsL1Uxz6Awzbb9ECDgd/nZLG2yUSoghY/h0vrwCBmwayCGkdQh/FJqp4cPsGW0mW03iEryjtZ2vwgZQ+dys0bsjuf22OMXlT2hsseDwTJh/S0wmuAfBLHsXIRV+8YYdxnkNrQK0Kg5X9SKDIIBd5hG3URTNJ4YfYLL65dZK6yRDCQZiY9Qb9fJ1/MsZZb4/IHP85uP/uYHdhZYlsXl9csgcJftfsAdoGW0uLJ+hQM9B3Yt/lNllWBsiIcDKXp2uauVBAFFEEjfECqVZoWXr73MdnmbodhQZ21to+1c3G2bLx3+EqrPB5/7nCMe1tcdQRGJOO3SoRAy3Jo+ez+8/TZkMs6+LRSgXHZSQYIArZZTuyIITjoom3Vas5eXHaFibEHjNLXaCpnXFzC26qjdSRJKPy454fz+4iIYBlNf/CKDDw2yXlx35gbJGr3hXqdu5R7Yts3l9cs02g1GbjMsEwXRMf8zdV649ALvr7xPqVGi3q47k4DjIyT9SbQ79nvQHWS7vI2nVeFT0QE2jRYl00QSBOKyQkre3Wsm6ovy3z3+3zEYHeSl6y+RLqVJl9MEXAF8mo/DvYcJeoJYtsVsq0a1sIoqpgl548SCSWzB6WSybRsRgYYdwi1cQ7VLtAlRapSY356npbcIeoK4ZBejiVEO9j5GxHPvGTcregtRYFchEpVk1o0226bOwJ5Q2eOXgD2hsseDoVWERg5kt1P3EA/D8ibEXEAZcANNsNuQLUE0BMkw2Isg33to2vGB43zl8Ff4/pXvs5xbRkDAsA1sy+bJsSf5vad/70PbH8vNMpulTeK++K7PR31R5tJzjrC4h8OtYds77s7vxBm55qQyFrOLbBQ3GE+N76idUGWV4fgwS9klVvIrTvRBlh2hMLT7+9435bIjeAzD8fwApy5FEJzjYVmOUKnXnbbnVMoRM7kcGGmsyveZ1XVmc3GqEgj7XSC0CJgV9rdU+rWQk6qam4OlJdz79u0ePbmJ2YbyClTWwGxTFV1sbV8j7t99mnG5UeadhXd4ZOgRJlITNPQG1zevM7s920kp3Vksbd/Y3wFJ3lGL8mEE3AG+cvQrfP7g53lt5jVevPYifpefi2sXubh+Eb/LT9wXx+sOs1haJ+yp8vDYQ6iyio2Nzx2gUMtjqFE8WEi0kWmQbYi8PvM6uVqOQ72HiHgjNPQGpxdPs1ZY47MHPkvUF71rPbZtU7YM3PeImomC4zTUsHYX9Hvs8YvGnlDZ48Fg2yCpjlDR65CKwOY2bFUhJILcAFOHUhv8wJFRkNMgxpz22HugKRpfOvIlJrsmeXfxXdKlNIqscHzgOCeGTtxX+6NlW84d8D06KERBdOzA7XtfCEKSzLze3GGZfztN2+504yxll/BonrsKPMEZHyAgsFHc+OAL/Uel1XKOQavlpHi2tx3RIsu3Uje3I0lQKjmuvM2LLOhNLjKBt7hKX7WI4ItgIZCX4H1vE6Up0IXk1LosL8O+fR+wlhIsvwylRWf7goxYzzOQn0aUBwl7CqhCGRMXOesA680kK/kVXLKLmC+GKqvIokx3qJtMNcN6YZ1UMEV/5FY7c7FeJOAK7Hrhv18UWUESJURENoo3WolVp4Nnq7SForrxxAbp6p1gIOZ0tAhA0uuh2E6jGcv0sYZCDRu4tH6J9dI6T4w80SnsdqtuQp4Qs9uznF0+y6f3f/qudQiCgILQ8YDZDQtuORrvsccvOHtCZY8HgxYEfx+UV2F9DiqA0IJaHavUwHbXQNKRIn3wZC8M1xyR4jn14a2xssr+nv3s696HaZlIovSRbMT9mp+wJ0yxXsSj7t5d5NN8hD7A4rtL0fC3G+RMg5is7Hgub+p4RZHuG+kJwzJ2FSk3EUQB3dSpNJ3Ih1f13lNE3Tea5vwoihM5kSRHpBiGIxZumsRZFoRCTprJMKA3Srs9zTw9uASbULvRGdQoAjFDZFNosKC0SeoK4s3t3wvbgrXXoLQAwWG4UcyqeFIM5P+KaOVFJJ+C7XKEU7f4OhoDfK8eIuSJdNJHoijSH+knU81gmAbrxfWOUGnqTdLlNCeGTnzgMbsfivUic9k54r44I/ERbNum3q6jmzqFeoGmIOHxdrNm6vgFEMwMVbFGzKeQqF4m1L6CIchsl2TW820O9RxiPLVzarMoiHQFHE+g28dC3E6/6uJMo4xly3d1ddUtE5cgEv0pjdOaepNqs4okSoQ8oZ8ZK/499riTPaGyx4NBUiA8AedehK08KBZGxEPV40XfLqKKDcqhFPpnHqOrewy3dwLUEZDunbe/E0EQfiKXS0VW2Ne9j5euvkS9Xd8hVnRTZ6u0xeHewx94d+4TJQ65fJxvVljVW/hEERCo2s5F5LDmI3Rjbd3BbhYyC7tux7AMNoubCAjMbc8BkAqm2NflpFJ+4otHIODUtmiaU4OSTju+KfW6E2UBJwUUCjmPFwpw4ABMDlJoz1C23aREC/weMNKOsBEEEGRCepmsW6ImCfirVSdtdC8q61BcgMBAR6QAqO236Q6u0iq0qVfjGK4oYKJSZsR1kWeSQRatb+wonO0OdVNr1zi/cp6FzAJdgS50U0cURQ72HtxR/HwT3dCZy8yRq+ZwKS6i3uitTqtdaOpNas0aYwln+KAgCJ0oXcAV4PzqeVJNgQmpzZa+hG0WGMKgT2oQ8qhU3MNUrW6Oupu0TI1Q7OFdz1Gv5iVTyVBv13cVKj2yyrKksm62SYgK2o1ut6ptUjBNJlXPh/vn3INKo8K7i+8yuz2LYRqd7qSDPQc/+jDPPfb4BNgTKns8OGp+2PJAcADDLpOrLNNqVxDCMkuNfi6sB7j61gpDYzG+8fAgcff9i5Sfln1d+8hVc1xYu4CAgNflpa23aegNRhOjPDry4dO7+xQNryiypjstowATsodeRdsRZRmOD3Nh7QJb5a1OCyw4tQhvzb3FenGduD/emY2ynFtmKbvE46OPc3zg+E8uVk6ehJdfdkRIKuWIllLpVlTF7XYeU1XHx+WLXwRvBKutYtttJEGFRAi8bihWIewH20CywRJlrGzWEUQfVE/TzIFtgHybP4ulQ+MdVEmh6UpgVio0/G4kSaZpuxF0gRPxIn7iOz67KIqMJ8dpG20kUWIsMYZbdTMQHXBm9NwmCGzb5v3l9/nP7/9n5jJz2JaN3+2nK9jF0b6jnJo4Rcwf404EQSARSLBd3iYRSHQiYbZtk6vlCLgChNRujgT3YWb/TyhdRyro0G6BqaFJg8S691GO2AyW36ZgFp25SXfQMlooknJPU0K3KPGQ28/lVo200UY3nQiYRxTZr3mY1Dwf+bzQDZ3zq+f5j+/9R+Yz83g1L13Bro6QXius8czkM0x2TX6k7e6xx4NmT6js8eBYXAaGIHqUtc232axXkNwDnGk0mTVMFL1Ko5DlB1d/QLqc5u88+3fu6sJ5UCiywlMTTzEQHWB2e5Z8LU/Cn2AsMcZwfPgup9J7EZEUIpLCwRuplN0uHolAgifHnuS1mdeY3pom6Ha6SNYL62wUNzjef7yTHmjpLVRJJVvN8sKlF4j747uaqd0XAwPw9a/Dn//5rcLZZNJJ86iqI1hiMSficuSI46EianjUQbT2GnUrjsfrhsk+uLIM61lEV5WGK4G7UsRdr8MTT+7qKNvBtm+lmW5irIKZRpIjBDwiiq6iSzKGZSGKAi7PCH5znq3GdeoM7vjVttlGUzQ+f/DzTHXd3cLuvKXN2/Nv8yev/wmFWoH+aD+yKFNpVtgqbvFm601M2+RLh76ER9uZ+vNqXkbjo+RqOTYKG53uopbewu/yM5IYIewJAxGkFWDdDa4YuP1geaHQgq3z+CbGiflUlrMLRCN3C5WbJnlR772jdkFJ5jF3gLxpULNNBJzz7SeZ8WOYBq/OvMr3L3+fpewSQ7EhREGk1ChRapQ42HMQURB5Z+Ed+iP9d+2XPfb462RPqOzx4CgWweujYfuZqbhpy/u5UN8go0O3GsWjyNTkEGoswvTWNN+98F1+48RvfGJfkpIoMZIY2dEe+5PyYXe3+7r34dW8vDn3JjPpGadgUlIYiY8wnhrHtm2Wc8vMbc9RaTm1KplKBtMy+c1HfpOJ1MRPtrBnn3VqU954A65dczqAFMURKYGA0/48Ogq/+qtOdAUIeg7S1SqwqOfRJA0p5celS7hX5pDSBeR6jkTEQH3mWRj/kKGP7iiIMhgtuGHKh92+MUVbQtQN8ASwRJVau4kiSXg1Hy7BRVMvs9ZYc2Y3iSLFepFsNcu+rn0Mx4bv+ZbpcpofXvkhDb3BVPcU8o0UiVfzUm6WaegNrm1c42DPQfZ17ywC7o/2c33rOieGTrBd2SZbyWJjE/VGifqibFe26Q51O9OsN7bAN+xMoQZQgbgfyhXExUV6D4a5rGgsZZfoCnWhyRoto8VmcROX4uJo/9EPrUUSBYGYrBBj91TV/bKYXeTC2gUMyyAeiHfSWW7VTbFeZGZrhsdGHmOzvMlaYe2uupo99vjrZE+o7PHgCARgbY2m7nYK91RI6xX61DCiICJYFrYi43P58Ll8LGQXWMot3XXx+LgxTIOm3kSRlLv8OB4UmUqGdxfeZbO4id/lR0Dg6sZVLNtiLDlGtVnl4tpFNFmjK9iFKIjIoky+luelay91Brt9ZFQVnnkGxsbgyhXn52bKJpGA4WFnCnDytjZhKcy+4BPUy9dZb2fpyZzDu3oFcVVHSLvoM2vE5Pchn3G+QYYfvvf7+3qc+pTiPIRGQZRA6gLRg1XPU2opLEk6LUtBkWVahk6jmSHsEumOP0217GWztIllW/hdfp4Ye4KjfUc/8LjdHIbn03wdkXKTgCvAWn2NcrPMcm75rnNtKDZEV7CrE/G4OclbN3UWs4v0R/qdIt4Lr0DbC/E2GHcsIOCHrTTh1jAnx77IuyszrBXWOoXf3cFuHh58+CePlP0EzKZnwXY63lzyzmhh0B1kvbBOrpYDG+rt+ie2rj32uB/2hMoeD47hYbh8GVHXkQSJzVYeRZAQBRGx2caSZdrhIKZlooiOaFgrrD0woVJv1bm2eY3rW9eptWqosspYcoypriki3gdXH9NoN3j52stsFDcYig+h3CgqrTQrnFs5x7mVc5iWiSzJOworLcuiO9iNaZlcXLvIYHTwJ+sGkiQYHHR+vvAFaDYdTxVFcVxpd8GnRHkk/Cib2+cx1t5EvaSg5QXc8TA+txuppcPlRcj9K/jtBKTucdEVJeh9yqlLKc47BbWiDI0Uun6eDSWA6A0R6dRq6PiFBjOVLgpChF858nkKjQKWZRFwB+6r/bxQLaDICpZxb+PAm8MI78Tv8vPs1LP86PqPWMguICA4LeiiwGB0kFMTpxyRVKqCOQSsglinqatYto1LkhEFAzxVqMUZSB6hJ3aArdIWLaOFJmukgqmfqAj8pyFfz+N3+1EllYbe2PGcIAiIokij3UAUxZ9smOceezxA9oTKHg+OgQGYmsJ/8SIJQ+ZMq4wggVypITeaVPu70ANeSrU8AXeAoCu468Xj46DeqvPi1ReZTk8T8oTwal5aRou35t9iMbvIp/d9+oHVxyzlllgvrDOSGLk19wboCnax5HPM3oBOpwk4d/AA8UAcv+Zno7hBvpbftQD0I+Ny3dfwQbcoMdzMY82XsKsi4tgQgnRDKHncEJiE6xfhjRfhb/ytD9hQBIa/BOVlKMyDpWNH/pDlzX+CS5zFpdjotoYstJGEFjWzm7L2DVZyK2xXtumN9HY2VaqXeOHSC1xauwTARHKCLxz6AvHALfM+VVZRZZVqq7qrz41hGggIJO9hNpcKpviVY7/CSm6FbDXrFNj6E/RF+m5dxL1e2Ayx5m9ztXGe5XqZtiUS0RT2BYPsL8aQe44DzgTo2z/DbhiGwcX1i2QqGdyKm0N9h37qVuvb8apeSo0SPeEeLqxdIOQO7RC9tm1T02v0hfroDX/wWvfY45NmT6js8eBQFDh1CtHno/+91+mbucpZO4MZ7Kc+0k+tJ0mxUcIwDYZiQ9T1+o6umI8Ly7L48cyPeXv+bcKeMFulLWqtGm7FTdATZCm7xLsL7/LFw198IF4Sa/k1ZEneIVIA4v44faE+3s69jWEZTKacbotaq0ahXqA/0k/MG8OyLQzLwLTNXbevG/pP3Kr9oWwuIqZrkEiCdEc0R5IgEoD5WaceKRTafRu2DRvbML0CGxkAWr1dvNN4np74CH3SNKpYoWWHyNuHyAuPg5ZEN2coNor04lw4Ty+e5n/97v/KQm4By7JAAMEW+I9n/iP/4HP/gFMTpwAYjA0S9oSpt+rka/kdbea6qVNpVTjUc6iTSsvX8ixllyjUCx37/95wL+Opcca5VauRr+VZWFtgLb+GT99A2TjHuaZNXfUhqwqW2GK1bnB2rcFxM8Gzz/ZxP9L34upF/t27/4759Dxts40gCCQDSZ7f9zxfO/o15N2GRH5ExpPjzGfm6Qp2kfAn2CxtEvaG8ageys0yLb2FJEgc7T9KwP3BPkZ77PFJsydU9niwuN3w5JMkjxzh8IX9vHfm3/OW0CQQBCppfC4fh3oPIQgCUU/0J6vD+ABW86u8Pf82/+H0f2C9sE65WcayLLyal5AnRMAVIOwNU2vVeHjoYVJBRygZpkGmksGwDHyab1evi/vFtMxdDd9kSeZg70E2ShvMpedYyC7gUly4ZBfD8WGmUlPIkkyumsOn+vBpt9I0tm2zmF1kemuardIWguCkJiZSE3SFun7itd5F23ZSRZ57RGA8KjQNp6PoXkLlwgWnmNcwnNdYFuKFCxiNOVYn91Ef+xtgN0HQdgzns7FxzOKdLpl//F//MYvZRcYSY7huDHtsG21mt2f5J3/5TxiIDDAUH2IgOsCh3kOUm2Vy1RzrhXW8mpem3mSrvEVvuJcvHv4iMX+MK+tXeGv+LYr1IpqiYZgGZ5bPMBof5enJp3EpLtpGm+XcMm/OvUm+nsen+WjJVWbMedxzNaI9+xAivfgsgXCtQrWS562UQcPM82Wj5y5DwNuZ3pzmn//on7Nd2mY4PozP5cMwDdYKa3z79LexbZtfP/HrP8mR28FwfJjh+DDz2/OMxkfxql42ShusFdZotBsc7DnIlw9/mUO9h37q99pjj4+bPaGyxyeD38/xJ76CNjjC/3f+/yNfzxN0BQl7w7SMFiFPiE9NfOpjDXev5lf5/uXvM7s9S7qSpqW3EAURTdOwbItSvcR2ZRtv2cuVjSuMJkb5m4/8TVbyK5xfPU+6lMawDLyal+HYMCeGThD0BD/8je8gFUxxaf3SrmkITdaYSE4wmhjttNKG3KHOXa1u6mxXtjk5chKv5kU3dNYKa5xePM2VjSv4NB+JQALLtji7cpbp9DTPTD7DWHJst6V0MGybbaNNztQxgYAokZJVPHe2vsbHQfieYxLnvmPIoNkEE/BGnejZbmxtOcMRvV6nFfoGajzOwLVNLlw7T7R7ELw7a2UqzQoe1UPc76R0Xrj4AovZRSZSEztqKFRZZTI5yZWNK3zn/Hf4u8//XVRZ5ZnJZ9BkjXOr51jNr5Kr5pyW9LGn+OqxrzKaGGUlt8KPZ37sHIPbJmi39BYX1y+ykl8h6A6SqWQ4u3yWsDfMI0OPEHAHWLFWaIwO0V5LI+TWGG2ZyLKC6fOhHDiK6IfFzDxziVGiknzPSN0Pr/6QzeImh3sPd1IxsiQzGBtkObfMD678gGennv2pU35u1c1zU8/h03wsZBbwal56Qj0MxxxB/MzUM/hcu9cr7bHHXzd7QmWPT5QDvQdIBVPMZ+Y7tRn9kX5G4iMfT/3FDSzL4uzyWaqtqjO40DSwBZugO4hpm2yVtpAECa/mxTRNdFPnxasv0mw3aRpN3Kqb7nA3iqRQaVa4sHaBQr3A5w5+Dr/L/5HWMhQbIuqLslZYoy/St+O59eI6EW+EZ6ee5fTiaZZyS6iSM+yu1qpRapQYTYxypO8Ii5lF3ll4h+tb17m0dglN0UgGkng1L32RPuL+OGuFNd6YfYNUMHXPddYsk/PNChu6jg2I2B2xcsjl2zkRevg49A/D8hyMTDjzmwD0GrTyUHfDxBRE7+EHMj8PtRr03l33MDlwiNmLy2wsXCF14OFO1KnRbrBeWOdw3+GOUDmzfAZJlHYt9JQkCU3ROL10uvOYz+Xj0/s/zdH+o+RreWzbJuqLEvffMpG7tnmNtt7eMTPoJvlqnvcW32MiOcFKYYVrm9fQZI2FzAJPjj2Jbdu0VZnW/v1sZ9dQw8PEwt2Y/gC2puIub2PUC2ybbaqWiX+XtFyxXuTcyjlSgdSuRdI94R6ubFzhwtoFnp16dvf9+xEIuAN8ev+nyVVzFOtFREEkEUjcV4HyHnv8dbInVPb4xIn5Y8T8sV0tzz8ustUs64V1Au4As+lZAu5AZ3BdrpyjfcNJVpEU6u06PeEePKqHV6ZfYTI1yaOpW860QXcQn+ZjJj3D9NY0Dw0+9JHWEvQEOTVxileuv8L01nRHQJQbZcKeME9NPMVgbJCoL8r01jTXtq7R1Jv4XX5ODJ1gIjlBvpbnxasv0jKcWoKwN0wqkKLYKDruuoJAX6SP7lA3M+kZlnPLHOi52+PEtG3ON6us6i26ZBXlhjiwbJucqXOuWcEjioRv2t27AvDcfwN//i9haR4iHlBksGSo+yE1AceOdeYB3UUmg+HzkVEgqwoYyARMSLYNejwxng5N8Wa9yczWDIqkYFgGiqRwoOcAT4w+0REVuqF/4LwkSZQ6x/QmN11mdyuSbupN1gvru45JWMwtkq1lMSyDucwc5UaZqCeK3+UnXUnz0rWXGEuMoZsGFgJ4PGQ9IqHbIkaGpRNQQui2jY5913uA0wbcMluE3KFdn5dFGdu27+rS+WmJ+qI/1fDGPfb4pNkTKg8QwzCwse85V2SPB0fLaNEyWvjdfkzbJOaLsZ5fZ6u8RaFe6LSd1to1WkaLqB6l0qxg2RY1vdaZgXITSZQIe8Jc27rGkb4jH7lwdTg+TMAV6ESSbNvmcO9hRhIjnaiB3+XnocGHONJ3BN3UUWUVSZSwbZtLM5eotWqMJkdZLaziUlyIokjEGyFXzTGfmScVTKFICrIoU6wXd11HztTZMtqkbhMp4BiLxWWVVb3Fmt66JVQApo7C3/xDeP1FmLkC65tgiJDsgyf3O91d96DubnKut8ZGYhMbG9GS0e0owZaXgzWDCSlK1/AUy5O9nWLW7lA33aHuHcXHU91TvDn3JqZpIt05+Rmnq2sqtbtT7b2wseGOjEzbaLNeWEcVVfLVvFOY6w1TapSQZZmEP0G1VWWrtAWygm22MSwL4bZ9aVomzVad0cEhNEHAJYjkqjkWs4ukS2lkSaY/2k/ClyDgClBqlHZtj2+2m8ii/IHutXvs8cvAnlB5AFxev8yPpn/EpdVLWFiMxkd5auIpHhl65KefirvHfeFW3LgUF5ZloUoqqqgiyzLbxW3Hpl5WO50yPs2HaZoU60VivhhtvU3baN8lRtyqm2a7iW7qP1GHzf1GkmRJ3rH9UqPESn6lExmQRdnperlByBNiu7JNsV4k7o87vjTS7uK4aBoYto16j+iETxTZMNocuLOeZnQMLlyE+R9DpuB0/BRqsL3tpHd++7cdc7nbsNrrXByosJLT6dYrKAggtrGFDXJqP+esBB63RnR4goO9H1xE/fkDn+c7577DYnaR0eRo5/G20WYxs4hLdfGZ/Z+59TnrRea255jbnkM3dbqCXYwlx+iP9CMIAprspM3mM/M3LPEdmnrTGUzYrnUEbrqcZrO4SV7OIwgCpmXi0TwEZS/ZWo6y3mTfjeGJrXaDdGGdRKgXV6iXHtnFSnqW12dfp9go4lE8mLbJ5fXL9EX6ONx7mP964b/SFey6a2zDfGaeweggR/uOfuC+2eOTpd6q09AbqLL6kdPAe/xk7AmVj5lXrr3Ct978FsV6kYgv0pmfcWb5LF86+it84+FfQ/uQi1xLd6bb3o9rqm5bLLWbrOktLKBLVhlR3Wi/5IIo6ovSH+lnJj2DKqvMZeaot+pYltWpWfFqXnya000TcAWcepRWhYg3clcrMTiheq/q/cQNsQzTwDCNzvumgim2SltYtoUoiEiihGVbmLZJU28iSzJdwQ/o/PmADuybXTb2nS/70Y/gz/7M8V957DFHqFiWI1S+8x3n8d/6rVuvtw3y9XfZ8PlIZlso2QKEgiC5EASDmL3Aal1ndXyUaN/Oup3dmOqe4m+f+tv8X6/8X1xcu4hH9Tg1PPUSiqTw6PCjLGQX8Ll8xH1xXr7+MulKmoArgCzKXFq/xLWtazwy+AgPDz2MIAhMdU2xkFmgUCt0urpu3khsl7bxa35KjZIzysA2qbVqaIpGtVlFlVVi3hhdqhdblFmvFSi3a2iyRjQ6QPfQw/S4AlBY4QdXX0RTtE77OThRl/nMPFFvlMO9h7m4dpGIN0LYE6apN9ksbxL1RvnGiW90Opw6u9a2Wc2vMrc9R6aaQZVURhOjDMeH9+pNHiDlRpl35t/h8vplKq0KsiQzEBng0eFHd4jnPT5+9oTKx8hmaZNvv/ttdFPnSP8RAGwEIl1TrBkm/zm3RH3tIieT4/Qr2s7wOs7U3Oub11kvrANOMd1EaoKB6MCuXQNZo81fVXMstJu0bwx+kwWBPkXjs94Ifer9Ddb7RUQQBB4afKhzV61KKqIo4lbcVG2nwLZttKm2qiQCCfqj/eimzmZpE0W+21rfMA2K9SIPDTy0q4jZja3SFvOZeTaLm8iSzHB8mKHY0Ee+C/NqXtyqm0qzgktxkQqkiHgjbJY2SfgSmLaJKqmYlslidpED3QfoCffsui2/KCHajsBVdomqVC2TMdWDePv5Zhjwwx86wmRw8NbjouhMZW634dVX4fOfd2YHARgbFPUcujqEa8gLy0CpCNaNeg2XgT9qsjW4D1OWuZ89+jcf+ZsMR4f5i3N/wYtXX8SyLY72H+XJsSeZ7JokW83yw6s/dFJ+mp+J5K1OnkQgQaFW4N3Fd0kEEgzGBhmOD/PYyGOcXjpNpprBo3poG040zaU6qbXN0iY+lw+v5iVdSTvCSHYKrOe25/jvP/XfMzVwnKVWnbTRRJJdYBmUl9/nTHGd1ewi25VtJlOTCAh0BbsQBAFJlBiMDrKSX+HXHvo1prqc1FammkERFZ4cfZLPHvgsh/p2tgtblsXpxdOcXjqNYRr4XD50U2cuM8dAZIDn9j33QF2Wf1lZL6zzL1/7l1xau0S5WabcKDvjCBQXL117iS8f/jKfO/g53Kr7wze2x0dmT6h8jLwz/w5b5a2OF4GNQNEXo+YO4rMsSpl55rZm8bjDXLMMTviiDPucL5ULqxd4Y/YNdFPv3N1d27zGTHqGJ8ee7AifmzQtk/9ayTLbbtIlKXhECQFo3oiwfMfK8luhJKF7pAB+GUgFU/RH+p2CSn+CSquCgIBbc1I4LsVF0BNkNDFKxBPh2tY1xuJjJANJFrOLJANJFEmh3CiTqWQYTYze93DAC6sXeGv+LertOj7Nh2EZzKZn6Q5189y+50gGdndF3Q236maqa4rXZ18n5AnhVt0c7T/K5fXLbFe22a5sE/fFMQyDw72HeWLsiXuKqbisEpdV0kabblndIUiKpoEqiPQqd0SMVldhaQm67hGl6e52ZghduwaPP+48ZlXAtgAZXB4YH4NyFVrNGx8KBLcLW/toX0GPjj6Kx+UhHogzGh/dkSJLBVMUagXOr5zncwc/t8OHBSDsDZOr5pjemmYwNtgRsz3hHuYz8+QqOVRZ5WDPQd6af4sfXv0hAgIhTwjbtol4IkiCRMgdwrRNesO9HOk7wsHufTwEzGSXeG3mdc4uvct6fgURkXwtj9/lZ7O0yXZlm31d+zpDMFXZEZembfLbJ3+brx37GrlqDrfqvqdL8kJ2gbcX3ibkDhH1RTtC7GaE5rWZ1/jy4S/vpZg/RmqtGn/6xp/y3tJ76IZzM2NZFpZtUW1VaRktGm0nFfSZ/Z/Zq0l8AOwJlY+RtcJOB9KG5qPmDqG2G0i2iWK0uTbzKmolTaZd57Ti5nO9B+gP9/LOwju4VTf9/lutkhFvhEwlwzsL79Ad6t7x5TXTbrCgt+iVVVy3XZRcgkS/rLJktLncqvPER/D9aOktlnJLLOeWaepNYv4Yw7FhkoHkA3FsfdAYpkG5Weax4cdwq25SwRRz6Tl6wj3kqjmytSzpUppsJesIRHeY33rst0gGklxYu8BmaRPTdOoRHhl+hKP9R+8rtL6YWeQ757/Taf+MeqOosoplWyxkFnh1+lW+evSrHymFdLDnIJulTea25wh7wng1LyPxEQRBYCQ+wsnRk0ymJj/0WMmCwBGXj7ONCmtGG00QkBBo2BYuUeSA5iF+57oMA0zz3l4psuy4z+r67Q/iF3RE26JtgypIEAwCN85Hq0zVVBiUVKSPeG5d37zuDBy8I4VabpRZya8wl5nj5WsvMxAdoC/sdELdfG3QE2SrvIVlWYiiiCAIneLd2+kN9/LuwrusFFawbAtN1nCrbkbiI4iiSHeom6ArSMtw0rTZSpY3rr/CRmENXW8yGh/Fo3o4u3yWQr1AxBsh4oswk54h5o8RvFHTgnBr8vbN4Zz3YjW3yp+89idc2bhC1BelO9jNQHSAVDCFJEr0R/pZya2wUdz4UMv+Pe6fcyvnuLR2CZfiYiGzgCZreN1eJFGi0qxQaVZYL67z2uxrTHZNMprYSwN93OwJlY8RTdEwLcfm3Abqmg/BtpBsk2arymZ6Bhkbqe8gfa4k280qry6eRpl9A4BjA8fu2mbcH2d6a5rF7OIOobLYbjpD0Ha5c5ZFCVUQmG3ev1Ap1Ut87/orXCtv03L5EEQFe/0q8fVrnBo4yrH+oz93YsW27U4dh0/zMZGaoFAvUGvX6An3kAgk0GSNkcQIiqhwpO8IJ4ZOIEsyQ7EhcrUcpmU6dSz3aYY1vz3Pt978FpfWLxH1RVnILhBwBRhLjtEX7mMgOsBSbonV/Grnzvp+8Ll8fGb/Z+iP9HNt8xqVZgVVUvnCoS/cNVRxq7TFG3Nv8N7iezSNJkORIR4ffZzjg8cRRZGQJHPSE2DTaLNptDFtmyFJpkfRiOwWgUsmnZRONrv7EMNcDvx+6Lkt3SR3EZNcJM0Sm1aIbtFEunn62DYls4EgdxFTPJRNnaZlYQkCHkEk8AE1XJZlUdfrOwpPG+0G01vTvDP/DhvlDcqNMtVWlXwtz3Zlm1wtx8Geg8iSjGmbKIJy61y2amBsgm2A6Ae5CwSR8dQ4Xz3yVd5depdqs0rbbONW3fg0H6lAisnUJKuFVeq2zYre5Mz6JdarGafQ2bY6gjbhT1Bt11mvbOP2Ram0qmyV0gTdQadg+46uHtu2ydfytIwWLsXVKfT90fUf8W/f/recWzmHS3ZRbt4QZdtzHBs4xlTXFC7FhW7pO8YO7PHTcXMgaMtosVpYRTf1HTVsPs0HtiOSN0ubLGYW94TKA2BPqHyM7Ovax/cufY96q47L5cWQVaQbw+XSuWXKjTwnx58mcOPLxyfK9ISSnLn8g7sq/m/HrbrJVrI7HmvbFtIHVEXKCLTu4d9wJ5Zl8YO5N3m3WcWbGMWLiIhN2x9nu1XjL1cvEHIHP9KF9WcBRVboCnYxvTVN1Bcl6A5yqPcQl9cvs15cR7AFx7dDVDjaf5RnJp/p3HmLothpG75frm9e589O/xnnV8/jUT2EPCFckotSs8SF1QuIgkhvuBfLsijUC3f9vmVZbFe2abQbHTO321M4Xs3LQ4MPcbj3MC2jhSLdXUtzbfMaf/SjP2Ixs4jf5UeWZJYyS7w5/yafP/h5/tuT/61TqyNKDKtuhlU3tm2zkl/h7NYMW+UtFElhNDHKaGLUcQoOBOCJJ+A//AfHXfZ2sdJuw/Ky8/zYbW64UgDJdZDD9XcwbYF1M4gmgIRB3Sxh4EYmyCvVAjlLR8IRUHFZoUtWmdK8+HYR4aIoEnAF2Cxugs9xsH1/+X3OLp2lbbaJeCLka3nS5TQhd4hUMMVSdomIN0IiOkC63eRE9wEs20RqnofWRTBvHAtBAbkXPI+DnORg70EKjQJ94T5KjRI2tlN47Q5QaFRI2zbzqpu1Sp431i4jSi6q5VUU6TbnXHeAtqyRruYQqzkE4EolRyTSIF9YYTg63BkCuFnc5P2V91nJrzhCRXYxGBvErbj5N2//G2qtGr2hXsfgTtaotCoU6gXOr54n4o3QHep23I8/qFp6j4/EQnaBH0//mCubV8hVc0iChGVZncGmgiBgYaHKKrVmjVwt99e95F9I9oTKx8hDAw9xoPsA76+8z2hyHCFkYwki1XqR5e1Z4sEuukNdZEub5MrbFPQmgi/qzN0oblBpVnYttLzpqXE7MUlGx8Ky7Z2Fjzdo2BbJ+6xP2Sxtca5ewhVIErVtBJyokNeGpupmXfNxenue4fjwz11UZSI1wWx6ttPZ0RXsIugOslnc5PrWdY7FjvFrD/8aQ7Gh++qyuhez6Vn++NU/ZimzRKVZIV/LU2lWiPvj9IR7KNaKLGQWOkMX79yP64V1Ti+eZq2wRttoo0gKXaEuHhp4iKH40I7XKrKyax682W7yrTe+xXJumUN9h5DFW3/e6VKaFy6+wEh8hE9NfKrzuG3bO4ozA64A9XadV66/wrXNazw39ZwzO+jLX3bqVE6fBk1zIij1OlQqMDW1s+PnJu7jBIFHm5fZMrbYtD20bQERlQ1pgJLlo2Q0UQURA4O80aJgqiy0m6zrbZ7zhvDtEl2Z6ppiMbtIo9Xg2sY1VgoryLLsmPo1igRdQbCdEQpezYuounivsEHYF0eODLHlS/HG1l8xoJ+l29ODqo2CIILVBGMZanXwfYGx5BjXtq6xWdqkP9rfafkuNyu8m5kjkBinx59CwyYkipiIbCHQateJ2RZGuwpmmRGvh+VGhlY1B4JIWnHxxuY1Hk9NcGryFLIks1nc5HuXv0exXiQVTOFW3NTbdS6tXeL86nmK9SKH+g6xml8lXUrjVt0E3UEKtQKFWoHV/Cp+lx+v6n1gU8B/2VgvrPPHr/wxc9tz2Jbd8V6qtCo09AapYMqZ+2S0iPqi2IK913X1gNgTKh8jLtXF7z39e/w/r/4/XN64TE1vYkcH0Atr+BQ3x/uOsZ6eJVdJYyJgSzLrjSJ6u8F2ZZtqs3qXUNFNHcuyGIwN7nh8SvPxbrPCltGm64YnyE1ypo4miBxw3fFHUyhAterUFMTjzn+BhVqekiAyLICg153XSiqIMi7bxqN6mG2UOuHonyeGYkM8Ovwop5dOk61m8bv86KaObuo8Nf4Uz+97/qe27l8vrPPdC98lXU4zmhztuOJqisZGYcOpgwh2k61m2a5sI0sycd+taM1mcZMfXP4B5WaZ7lA3btWFoZfZKi/zw6s5Pr3vM3eJld04t3qO2e1ZxpPjO0QKQDKYJFPN8OOZH+8QKkvZJU4vniboCe7wE0kFUyxmFnlt9jW+dvRrKIEA/OEfwo9/DK+/7qSBEgmnVTkcdtqXPR6YnISREWcukCCD5xHc2iRDxiYDts6Zls3rLTcWMhtGg6ZlY3LLEyZrWfRKCqeNNrZt8YVA7C7Pl5H4CFOpKd5eeJv57DyKqJCpZtAN3RFb7gCmbZIup9EFAX9ygkY1S48gMuj2s7n0Y6bTf4VlC/T5ijzSVWN/qt85t4Vh0GegdZ2w9zGe3/c8r8++zlJuCftGZ11LkAgnJ3ly7Al8soJt2wS9EbbyKwyEurhYWEYszuBtFXBbbRSgxwWWW2RZ8nNg6BHCXfsYTowQ9oSxbZuzy2cp1ouMJkY7f8sBd4C22WajuIEkSk6ayBclX3VE8M3W+kK9wHphHZ/m4/jAcWK+j28UxS8z3zn/HabT0+zv3s/1res0jabjDC1K6JZOtprt3FREvBE0SbvvYvs9Php7QuVjpjvUzf/8hf+Z82vnuZJZZFH1YPUeIbf6PvnKFvlyGr8/jo5ETFZIyirpSprl/DKX1i8R9ATxqB7A8e1Yya8wEh+5ax5JSlF5yhPkpWqRJb2FXxIREShbBhICj3uCjNxslSsW4b33YGHBmbsiy07dwZEjMD5OAxv0OnJuG4waYIOogTsG3gSqZdKUFHTb5udLpjiRi4eHHqYr1MX89jzpShpVUhlJjDAcG/5YBrFd3bhKpVkh4AqgSipRX5RcNUdTb+LRPGQrWeL+eGcK72PDj3Xah23bdu6YG0XGkqN42MIrrCJrRXrisF5pM72q0x/5PaQP8d9Zza9imEbn/LmTqC/KUnaJequOR3NeM5OewbTMHSIFQBRE+qP9Tj1NYdWZau3xOC3In/+8cx69/jpcvQqlkjN0cHsbFhcdofL8807UBUAKghRksVXnVT2LjUnDtCiZBtg2DdtGQkAVIG+18SIQkxUuN2uMS2X2izmwayB4QR1AlZI8M/UM1WaV2a1ZtipbFGoF4v44CV8Cn8tH22izVd6iiUDMHWTQ5WMsmOLK0nvQWqNHsakqSTYaFV649h4rhTSf2/cwLlkFMeKIFfthukPd/MrRX2E1v0qpUUISJXKaj6zqx686EThBEBhIjrORXcLncjEpt8lkl1C8MXxalGy9iOiK4LVbfDY5wdTBz9BUvJRti7Jl0q4XWM4v0xXquivSZlvOjUKumqOhNwi4AgzGBlktrDozjLAp1AtEfVEO9R7iibEnfu6inj+L5Co53lt8j1QwRcwXo9aqUW6Unblgho5pmxT1Ii7ZxXhqnKbe5NnJZxmN79WnPAj2hMoDQJZlHhp8iIcGHyJn6FxsVvnPmVlm594iHukHUSYmysRvhJJty2YsMUbYE2a7vE3LaCEgoCkaU6kpPjX+qV3TEo+6g4QllXONChs3iiJ7JI2UouIXJK60aqRaOtEfvoi4suJ4XqRSTnfG9jb84AdgGIRcWeRWjqZZx+UKAKIzGbe8BEaDquSjO9SL++csmnKTm3Nw7hwI+HHQaDdYya/QFewiW81S1+vOxeSGR0a9VafcKiNuiWiyxmTXJE9NPNWpPSnWiyznlkkGkgSEBXziNSqCSFEMYSGgBQsYzXdZzY4wmPgcfMBF6MNaUk3LRBKlzswc0zLZLG/ecyK0IilYlrW7Hf+1a3DxIgwNOWZvN9F1x6k2GIRnbw3Sa9sWM+0GLcuialmsGS2alokJtLGxAJftdCCtmW0M2+CQfQmjvEhLBU10g92G1hnQDuNyP8bRgaOdOUfYzgiCmwJMUzSi/jgrzSo0SkyMPMrCxhXApi+awt1KU2u3qZoNZL3EC1eWaOlFnhl/mIRHdd7LNkCQUWV1R33W2UaFot7csTt6YkOM9x1me+FVjokNrviTrNUrbJXzqIpGf2yIqeQwj7kFGtVVzMg+DMvExKapN2nprV0FpqZoRL1RMtUMlWYFt+Im6ovi03yUmiVy1RyWZfGrx3+Vzxz4zH17/OzxwWSqGaqtKn2Rvs7xt2yLue05io0ipmnSMlokAglCrhDHBo/xGyd+Y68t/AGxJ1QeMFFZ4VPeELnkOPPXX8FvNOly+fDKKrqhk6nnUWWVA90H8Ggenp18lvqN9EvUGyUZSN7z5BcEgUnNw7jqpmKZXG3VWNNbtLDZNNu0TZuZ1VWGayWGhgepA5apo0kCoZ4elHQaTr/KyCGJpMvPVrVNDyKKKIPoA8lFqbiC6RvioK4ir687kZh7tan+EmLZFqZl4tW89IR7mN6axqt6CXudFuJio8hKdgW/5uf5fc/zjYe+gXqbT0nbbNM22wSVFh5hlg0xSFkI4rJsRCzqdFETRLabpwm1DxLS7i22xhPjuFU3pXppV/GRrWZ5bvK5jtOpgIAkSLTM1gd+xrsufs2m45sSDu8UKeCcG6kUzM05wwrDTqQmbxqUTIOGbZIzdWzbcTlpYXVKvlvYqNhotkhYv07CPkdB7aIq96LdrLcyS9B4BwQfCf9Qp5hxMDbIWnENQRBwKS4EQaDebjpt05aBV/OzUL1Kd2wQw8yQrxfI1kuYioeg5sFQW7y/NotPgYd6kqQi+0HYvX3cJ4odg8Xb99Gh4Udp6hla+gqHQ0G62w08WpBkIElXKEnQ5UOurKGVF2mEJ9AEAU0QMRUXmqI5Ebg7xErAHaAr1MVidpFivYhP8+FRPWiKRlBwaq0eHX6U5/c/vydSPkY0WUNVVBrtBl7Ni0txcbD3IKlQio3iBvlKnnw9z8mRk3z+4Od5aPAhAu7AX/eyf2HZEyqfAJIgcCA6wLHufZiWSb6ap2gZSKJExBthLDmGJmu09BapYKpzV3iTpt5kfnue85vXWKsXMFQPPYkxDiXHGNB8RGWFtNFmRW8Rk2TcN7+wTIvq6hrvpmLMmxbJar2zTa8oMRAJE11+h0ha5onhJ3hZ8ZAuriFZJpIg0mo0EGsW+wrbPHy5Afq7jlA5ehQmJj7w7v6XBbfiJuKLkC6nGYoNUawX2Sht4NN8aLKGiIiiKDw++jhfOfqVHSLl5u+7FTeYSzREnYoQxG3qVJtVKs0yTb2FYRn0eAUWyxc5Gr+3UDnQc4BDvYd4a/4tJpITnbSWaZlOm7Q7wKMjj5KpZFAkhaA7yHB8mLfn397VgK7WquFSXCT8dxRnVirOTyrVeciyLZqmjmRaaDc7gebm4PhxEEVM26ZqW4iCgG1bIDiTnG2cDjWw0XFMEqFFlz2NKfooCXek5qQg2HVoXybon6I30svFtYt0Bx0flEwlQ66Wo9FuYCMQC/fSFR9BuyHOJFEiUxMwGhZh1aIiaaiWTtDtxbJtBFtnJXudQPQLeO4xDykla3jEBkXTIHTH4Ep/IEaqez8nI5NsmzoZyyAqyZ10jC0qCEaLgmkyoXrwihJub5S+cB8LmYW7Outs28bn8nFi6ASVZoV0OY1t2bStNs12k9HkKL/z+O/sudF+zKSCKcbiY1zeuEzIG+rUfMV9cWLeGJfXLzPZNck/+Ow/IB74aN2Be3x09oTKJ0TUF6U31IvP5cO0THRTR5EUQp4QkiixkFlgNDF6lwVztVnlpWsvcX7rOhlEdEnBrmSZz8wznZnn+PinOOQJs9hu4r3RdtrB0GnWa7RcKnm3m/GWgWLb2LZN2TKZt9q4rSYe08MJRUAOx7nkDZJv1rCaTaK1Ev31Ao8ZZaL9B52wfjoNL77oGIDt3/8J78WfPURRZF/XPlZyzkTk4wPHWc2vslZYo96uU2gUODlykt848Ru7XkwC7gDD8WHS2TNIPg3Z0EmX05SbZWRRoqm3cKsuCtUK28Y5kt7H6faE7rmW/+FT/wO6qXNx7SKGaSBJEoZpEPKEOm627y29hyIp9Ef66Q33EvFEWMou0Rfp69yV36yPOtx7+G4RI4rOj2miWwYz1Q2ulJcpZdeRMzmGqyJTOZOE2w2bm/DII7hjEdq2hYKIW5TJm20UQaRlm52YigCIgMcu4bPL2FK/Uxd1p2CQYmCsgpXnxOAJzq+cp9FuIArONOlaq0YqkGIsMcZGqwaah3KtgGmZ2LZNtl5Ct3pxizlCdgaPrVE3m3gViz6/wUwxgL+iMXWPSQdBSWaf6uFCq0ZDb+OXHKOAimXik70MiQJhWUaVZRqtOjnTwCOKKIjQLrPh6yMuKYzeEE+iKHJ84DiZSob5bWcKtlt1un42i5v0hnr59Yd+nXwtz5mlM2SrWRRZYX/Xfp4af4qwL7z7Qvf4iXGrbp6bes4xwMwuE/VFcatuDNNgq+S08P/aw7+2J1I+IfaEyidEzBdjJDHChbULTiusfKvmJFPJOGmc1CR50yBttKlZJi5RZGbuTa5vTWMFugmIIiFRQhAE2nqTze1Z1nxx2r0HMWybwTtqSBqiSFGWiNUaFP1+arJESDcQBIGgJJNtNSlbAh5ZxI3No6LOmEsm64pgTi/hm10j6W7i0W6ENBUFenthYwPOnHGKJu8M/f8SMp4cJ11Kc37tPJIoEfaGcSkucrUcJ4dP8rmDnyPiu/cd79H+o7zReI1qa5Fs3aDUKOCW3bTNNl6Xl65gFzEpy6VGk/dWzvGVyafvua1EIMH/9IX/ibPLZ7m6cZWW0SLsCVNqlCg3y0iiRNwfp1gvcmb5DCv5FQ71HmIu48xEEhCwsVFllUO9h3hy7Mm7U4/hMKRS6Our/MhT4lJ5CXe5TmAtjS7Ae3KT+V4fz6f89M3NQbFI+HOfI6jKrOktopITAbQFAcUW0G8IFQnHOddtgUsUaFkQViRcd6U+nQgMts1IfISHBh+iVC85hm43LOkL1QKnl06zUdrC7U+wGOrG1lsYlknV1Gkr3WwKUfqtDbDz1NpVJhPDFITjrLUaDOnGrvu33CizkFlgIbtIAQEhmKLlTxBwB0jKKoPx/SQq8wj1ND5fN+Oah22jTdbUabcKSKJCT2wfvZ7ADp+YnnAPnz3wWc4un2W1sEq71EaTNcaT4zw0+BCpoBO9OjZwjLbhPLdn1f5geWT4EWqtGi9ee5G14hqmaWJjkwqk+Mz+z/Dc1HM/3RsYbdh4Czbehto6aCHoOgk9J8EV+jg+wi8Me0LlE0IQBB4ffZy20WYmPQM4xYoto4Xf5eeJ0Sdp+JNcrJecO09BoNQo8s76JadlWRCI3xApAKriwu8OkcvM4Y6PUhNFvIKEIIAiCARFmaoIxa4UiekZbMu6a03hTJZMsJdozIXSKiJpIRKYJFpNuHQVTAOMBkSndv5iMul0EK2vO2LllxxZkvnUxKfojfQyvTVNrpYj7Ak7U1UTox+au454Ixwd+iLX0/+GpYJjGGXhmEqFPCHcsgQI6MoAK5lFiv1HHSO2e6DKKo+NPMZjI48B8Obcm7w59yaj8VE2y5tc27xGuVEGHCfdervO7z7+u2yWNyk3yoiiSCqQoivYtWt9VKVdYzOuMffuu7xmbzIcGyaRqSOJLlAUonWRxaSPN811vj78GMrcAsLVqxw4cYz5VoOmZeETJVq24zOk2LZjJQ/IgkhbCtEwfUSECv1K8P/P3n8HSZbf153o5/r0tjKzsnx1VXW1nXbjPTADgDMEQAeQMnwrUaJEhcSnjeCuIki9eFJI8TY2NmQeY7XUI3eDEvh2FSRFPlIAQdiZwWD8TE/PtO+qLm8zs9L7zGvfH7c6u6u7uqd7/AB9EBWYrsy8eSvzmvP7fs/3nBuNDa0yiGGQooQVLw/teYgXZ1/EsAy8qpe3l9/m9PppTNNkPDFOItTPRqvMVrNCprKBN5AgHuzDF4hRN/tZaQj0BSbQwg9Sc4KYzmzPM+VabFY2ef7S82SrWQKeAKIgUsvP4/OG2T/xMEcHDrjn59CjsPojKM/h98QYF0SGOkUsHMSRJ1D7dm+bDkYHGYi4Y+xdw7UCuDbP58p3+3Gnd/+0QpEVvnDwCxwePsx8bp5Sq0TYE2b/wP4PHiuit+DM/wc2XgYEUAPQzMLWadh8FU789+C764dzBXeJyscIv+bnSwe/xMGBgz33yYg3wnhinIYW5GS7RlSS8SJRrOdobS3SqmbRk3tRLJPkdSuogC9MsbZFTW+wKWnUbAvf9ipNEQR022E11cdlCaRWC1UVGHckUo0mgVyOriCwfPQBvN4SUuZtupIP0RtD6VqkmyU8nTrYQcgugSfnEpRUyjX8chzXlfQuAFefMJWaYio1hW3bCIJwRxeyofARMq3j7Ik+B6RwpBCKpKDQIkCedXEUVRpAb89Sa9duSVSuxRV7+b5AHwv5BWZyMyiCAgKUW2VytRwXNi9g2RZ//9G//54+EAtbbvDdViPHRqSJtValtvk2WqlNsC+NIgYQhkcY7k+y0imy3iky3tcH8/NMnjjOUcGh0FzHZ7aoyn7WlQiWKCIDFhCVFAKihiXvZ9B6h37xOoJtt8EugvdxEN026cHBg/g1P+c3zvO9899jcWuBoBJgengvfdvmZ5apUyytI5k6zXaF6uY5GoEEg7FBppPTTKen8apByq0yAS3AYGRn+nTH6PDi7IsUG0X29u/tTU4NANlalrfmXyEdiLuVj9g0KAEozUBtBRwbJTqFEtsHkT231HYJgnDHjsh38dHhSq7T9VlQHxiL34a1H0FkAtRrFjJmB7behQt/BPf9sw/3PT/DuEtUPmYossJ4YnyHgZflOJxtVtAEkVZ5nZOXnyefv0ylUWS5tI5Wz+FJTjOWnEC+5iJn2xYdT5i841ZQNEEkJEp0LIs5vU3JNOlgI/RFCDS9nPZ4WGq12G92mRwe5GTEJNO6yGuZTVY3ztJtFehTFUY8QQ4ub3Ks6CHdF0QItKFad1N0Uyk4cAAkyTX1+imE5TgYjoMsCDu+jyt4PyOKgqgS8T3JO84Saa1CiBwi0MXDijhJ1jlIv2FSFMU7mu5oG206RgdRFFnILxDyhKi366xX1rFtN5Om1W3xvfPfQ5EUvn7v1296Uc7Vcrww8wKmbbInMcH66Aa+wSGslRzF8xepxT2MjE4j+v0ogN2xaVgd8AShVMC/+SaPtubZaOTImQYdUabpH+Jk7DBlNUxCUohIMnXHRpCPExNEovY8GHkQvOC0XYKsHQHvsR37NtY3hk9QuHjmR7RaIj5TIKrnIW6zpZis1zMEVS9xX5iAFkBEpNwuIzgCg7FB/JqffD1PqVnioYmHiAfiO7a/UlwhU8kwkZzokZQr6A/1czl3mfmt+V6LhuCg+2Ppboq0pN0Vn9+FC70F6y+CFt5JUgBkDwRH3MpKZQki7230+NOAu0Tlk4RtQ71OwzSoCyZWM8vbJ/+IVj1HIpSgzztKvVVmq7hIsdNgTZKJ+8LUWxUcx6HaqeMdewCP4mW/x4/u2KwbHRa6Heq2Sd2xMHFL6mbAS8fvpWbZ1EdHONXIUVh8jT2KRrlVRQmmCYQGaXbKyG0vQb3FebGGGfMyckVfYVmQzboOt48/vjOE7qcAbdti1eiwanTpOjaqIJIWJPZ4/Pjfw4ztdjARHuU14QTnG1kiIS82AjoBRDvAkGVSq2VJBBI3XXHn63nmt+ZZLa0iIPSSdVVJZbm0jG7qqLLKemUdVVLxer1YtkXEFyHsC7OYd3NNfun4L+3q2zOXm6ParjLdP+0KdUWJriLQHRpAy5fJKxYx0SIE2+PHIAuSaw6nZKHUIBZIIScOIZkGS60SkfoST1ht1oefptUxCG8u82DX4rA/Qmz4OELsIBiLYFfddo86AcqI63p7LQyD5kvPY1y6hNc28Hj8YBhYS4t0zBredBQlGKHRbSBLMseHj1Pv1rmweYE3Ft/gYPogUV+Ux6ce74WDNrtN1kprdM0uFzcvolv6TUli0BNkvbx+4wPS3TbNXVyHZgbaBfDfpErjjUNjw63G3SUqwF2i8snAcdzRzfPnIZdDsSzSfo3z9gwtNhgcOACCe0EcTE7Sys1TbZV489IPSYcHwbFo600MNUQ83WC4WycRjKIiUrIMdJzeHIUEKAhYjk0LAUMS6WDTNrrck5xCzs/RMlq9UnfAG0G8dBo52ocd8LKwcYl0SkHxB8E0XbLSbLpGXz9Ffiot2+Jku86m2cXj2JSKKyzmLlNqVYgrXn5m5ChH0vt2zWq6XUiixCMDB/nOuVVsQyMaTKAIAh67Q7FRoK23eXTy0V01Cpezl/nx5R9TbVd7+7BaWiXqi+JVvGQqGRRJodQsYVkW3u1qWLPbxK/5ifqiBLQA2eomq/lzTCXH3DRh0SUstm2zmF/stZxkSSYdTjOTnSEcTGNHI6jZdZrJJiFviJLRICR7ScshKM7DXgOCA+CJEgIOyCp7VA+lQJxu+TJjc28TmmkRq5QJiO7AMl6vW7175AlQ3+OGPzODMjuHFokhmSJdwKv46SgCzuoGsZJGxefHsR0kUSLij7C3fy/pcJpcLcfn93+eqeQUAU8Ax3E4t36Ok8snKTfLIMBKYYVsLUvQE2QkNnKjg6zj3PUxuYvbgyBuZ0vtLtgGx62+ibd/e27rbZ67+BwblQ28ipdHJh9hMvWT45J7l6h8Enj3Xdd+XBShrw8N0JYvEbnwKvfsH6KY3lYWAulIP3XLJLN0imItjyypeL1hvL44yfR+HFGimb+MEYwjaX7qto0mCrRsd4pCQiAgSViOSMexMR3A1FFlBVWKkK9tEfZeNQfzORLhaputcIDU+CFyKzOUzSbJsum2e0ZH3UpQ6KfL3Gheb7NpdkkhcHbxDRY3LyLLKhHVR7ZV4b+e/x7r+QW+ePCLH8jTYjI5yVP7P89bi2+Rzc4gCG4QWsQb4fG9j7M/vf+G15SaJV66/BK2Y9+gMVktrdLsNgl7w8xvzWNaJpqiYdkWjW4DcGMfOkaHiNbF6V6kUsiDd8QlKuoB0A4BKg7OjrbHUHSITDVDtplHGEohFrNI2S2KjkjBafOwOkxkeROGQ5BQ3KmGa+ARJQZUHxgqnHsRxOOwd597nIHr1fL22+6/H3+cm8Ky4OJFUqEUA0KbrUqbLb1CSPbiAF2vRrjZpl0rI/r8JINJYv4YgiDQF+jDsi2Go8M935nZ7Cw/mvkRXtXLZGoSURAJqAEytQzvrLyDLMq9CARwSUqtU+P4yPHd989ugb4AnXfA3HTbWOpe8B7DFF1RpnS3LfSZh+M4VG2Lpm0hgKs33I28hoYhMAz1ZfDsMlreyIAnDrG9t/W+3z//ff63F/43VoormI6JgEDs5RhPH3iaf/alf3aDL9dnEXeJyseNQsHN3QmHIe72wSXAl4qQX9MYXqmij9SoJyMA2IKI6o8z7I/S73SZ7J8kFB0lEujD64uwpQWplzfYrGzSn5iga1uYtgPbVRVp+yIoCQKKI9J2bBxBQBZFGraA6Vj4xKsHsiO4q2Xd6CJ5vDjJJObQMdCi7g3D74fLlz+1/fZ6p06hXsDBIeaP3bbo9FZo2xZrRpeoKLOWvcT8xnlSsWG07XFwry9Kx7ZYKK3y2vxr/Ow9P/ueQlrTMrFsC/W6QElBEDg0eIixuJvn0tbbaLLGcGz4ptNDi/lFyu0y+/r33fDYcHSYmewMk8lJ1ivrrBZWEQSBoCdIQAuQCqXwq34UO8eA1iZTbyJKEyAmwa5A60dgbSH6n2YwMsjZ9bO91lPIG+Lo8FEuZS+xVtuiMRzAxsdwt8Ujaj/3Bcdh3wEYsKH45s2PmWwBuk2YGuxVEgE3KyiVcu36Dx/uudzegFYLKhWUSIxjiGy2i1TMJuudEn5BxlRENusVOm2R4eQo0/3TvamearvqhvttkxTTMjmzfgZZkq/qTXDHvieTk8xkZpjPu1oUSZSwbIvV0ip9/r4bzNrcDRag8T1o/xisIjgCtmOTbZ1ntbFIWb0fUd3DgOJhSNGI3mbi+V18utDYdgbPmDod20YAAqLEuOJhr+bbSURFGUafhjN/APV1CAy4FRaAThHaeZj6hdua+nl94XX+p+/8T5SbZUbjo/g1P6Zpkqln+LO3/wzbsflXP/evPpo/+mPEp4Ko/N7v/R7/5t/8G7LZLEeOHOE//If/wP333/9J79ZHg5UVd6U4sLM/OaR4CPlkunkd31yGqqTQ8GsYHg1ft4rf7hIKRLkvvZ9oxHUndQDDsWn4ImxWMyT7XLGt67AJNqBeX6IGZEQs20ETBUzZQ9to9zQJliKzFfFwuGzTNXVkSUbzBSGwXXVpNNxwur6PMKHVccDKgr4IVgkED6ijII/2WhGlZonV4iq1Tg2P4iERTLBR2mAmN0O1VcXBIeQJsTe1l/vH7/9Aq4q2Y9NxbKLAUmYGnyfQIykAmiDQBPrCaVaKK2zVt3Z1egVXRzKbnWU+P49t2cQCMfb172MqNbWjdRDwBHatnuyGTDWDX909Xr7UKjGXm0OWZMKeMLZjo5s6qVAKWZJZyC/QNRoc75doGqOoaopUaABED4j9YEehewmUMfam9jKbnaXQKPQSeuOBuGu6tnaa6OBhvnDgC4woEcKyzz1OvF4ozUIBsC24doVp1aCzCdl33QBMqwZOAMol6ORArEPQA40KrF6C6MNXX9vtun4+nW2bfMcB02RfZAg75RAonedcdYW8XqPkdPE4JkcT0xyefpLk9iSQbuoUm0We2PtELxW80CiQq+V6TrfgrpTrnToRT4SgFuT8+nk3JsEXxbRNUqEUT+x94sZKmmNA68fQeQNsHZRJHDRm7CiXTC+O0cXvnMMSvVy0U6waXU54g/TfHT/+TKFtW5xq18kaOlGhhc+uYtmgixpn7TAmDoevDz8d/QK0S7D0bcifBlFxW0Gyz31s+m++5/s6jsOfvPUnFGoFDg4e7In4ZVlmODrMurPO85ee52898Lc+86nOnzhR+dM//VN+67d+i9///d/ngQce4Hd/93f50pe+xOzsLMnkZ3+OvNQo8dria8xl5wB4YK3NPTZcn9nrCyS5tytTXVpEy1YYzJexPSrGUIy5mM18q8qe9DTh0NULqAAMWDpF26Isa+QaRYqtEvlmGV2UkTxBdEVFEtzsk45jI9kWttGmVd0kW8sQk2Qy9S2CniCSKNExOmTTYe63JDqrS8RH91xN1u103MmfI0fgo/puHAc6b0PnpDuKKvjcgDj9LIVOgoXmMG+snGVhawFVVl17d8GdyjAsg/vG7mMyNYmAQKVd4a3lt2jqTb544Ivv2yBLwg3La3QbNDo1gtdVaWxAFAQCmp9sfYtau7YrUVkrrfHDiz+k1CwR88eQJZn18jpL+SWy1SyP7X3sfekcREHEdm70yam0Kry78i75ep7DQ4c5OnyUmD/Gt898mzcX3yQeiJMMJkkFfOTqBTbqS/zs/vsYCF1zwxU1lyh2LzEc/QUemXyE1xdeZzY7S9ATxLRNWnqLA+kDPH3g6d2FvsERd3XYzEBwyJ2CMRZAn4d6ya06yBpsfR/WDKhXQamA6IDmA0mATBhSGqSOw8Vz8OpzbrCmoIAnDFt5EEWEhx/mYGiECX8/G4kiZb1BK5+lmc8wMzjlmsE1yzT1Jo1Og/39+7ln6J7erlq2hW3byNvi6I7R4cLmBbLVLLql4wgOlm1RbBQZiAwwkZjgxOgJon73HKl36uTreWzHJiJXiVuzCI4FUhQEjZzt45IdIygZBOyC+1lYq0TUIXKWydlOg4gvsovJ3V182CjUC5xdP0vX7NIX6OPI8JFbe9TU624SvShCItHTTW2aOpt6E4+5xJJZoWG7URAaNkHJw2VnnBFljPC1gntRhAN/E9L3Q+5taBddL5XkUYgfch9/D2zVtnhn5R3igfiuk4apUIrZ3CyvzL1yl6h8UPz7f//v+Qf/4B/wa7/2awD8/u//Pn/913/Nf/pP/4nf/u3f/oT37oPh7NpZ/uClP2C1uIq03XffmM+T2YJjfX72JPZcffLaJqN5mWzXYiPURQiKhHUD+ewceV+LyIDEnuHjiNfdyHyOTaiygc8XZWbzDE1Bwav4oFmm3W1Q0AJo3iCK4gNLx6ls4NRyOPl5NgqLFCwd03JvNgHNFRIOjYwxGzDYv1jmUF1GXF52dQCi6NrmP/roR9f6MRag/fr2hMfVXJuLmQW+fe5POZ9vs1qz8Go+or4ogiAwFB2i3q1jWRalZqlXso/6ovhUH7PZWab7p5lMvj9xWVCUiMsyqx1XMGlZO0VwDdvCL0h4BcEN+tuFbOimzstzL5OtZQmoAWrtGpqs0R/qx7AN3l19l3Qk/b4uKKPxUS5uXsR27B0akqXCEtV2lZg/xkBkAFVWGesbYyg2RNfq0tSbtI02LV0iqopuvIPjCq0FQUSTZfdvEf1g1xEEm6MjR0mFUszn58lVc8iSzERigvG+8V775AYoXhh4GFafh/IcKDroF8AUwfFAZB+sC9DNgbIJCR9YI2CLUK1BswEtHdZfhoun4PmXwWxBXxBkBeQ2eCS4vOw6JR85gkdSmfD1g14BHJrP/gzJkTAz2Rm6Rpe4P86jk48ylZrqVVPAnd7xa35q7Rohb4hz6+dYK6/RF+jDo3jIVrOIgshKaYVKu0KlVSFXy3F48DD1bp1LmUtU21UAfBTY41vioQGF0Pbk3LoTwEYgIBguAcQAuwZOi4TkZ8PU2bJ0RsS7js8fFUzT5M9P/Tk/uPgDCg23TayICnsSe/jVB3+VoyNHd76g2XS1UnNzUKv1tIXccw8cOsSa3qapr7BhVhBEH35JQcSh49hkrTa51ioHtABhb/+NOxOdcH/eB1pGC8My8Gu7V1NlScbBoaW3dn18NzQ6DUrNEi29RdfsIosyXtXLUHToEzUa/ESJiq7rnDp1it/5nd/p/U4URZ5++mlef/31XV/T7Xbpdq+mvdZqtY98P98Ptmpb/MFLf8B6aZ1DQ4d6oVY+tUR57WVeOPVtoo/9d0T9EbfHPjeHFh4kPWJi9FssmRUK2KghhwcbfryxA2QlHwnH2aFpqLQqmJ0aQrfORKCP+8Jp3rENLmpeQES3DKx2A4+kUs/M0CkuMRlKcnTkOEpshOXCIouFxZ7gciAywGh8lMlD00w/M0So1IRSyV09DA25I8nyR3TYOA50Z9z/l66u6ldKW/zBGy+QqRYpNst0rSCSJFNtVenoHTarm6iiSiwYY720zlh8rNfq0WQNURBZyi+9b6IiCgITipeCZqAFU1SKSwS8IWzHoenY2MCAolJploh4I7tWU5YLy7y+8DqGZaCbes8FPuQNMd0/jSRJzGRn2Jva+576lla3RalZAlxX2/G+cQajgyzmFxmLjyFLMm29zWZlk67ZZTg2TDKYxLItXp9/nWqrymhslFq7RtwfxzDLGJbNVDDGO2sLFJt1ol4/PtXDgf5hpmMiXk8frpoK0pE06Ui6tz8ly2DJ6LLVKCMCaVllQNEIXbuCjE66HhH5s5D/by7x9fZDdBg04O2/AtoQdcDogGi7x5njQCAIVROq6/DCKbBSsGff9uSE4a5G+7zQTUOx6N5QRNF9bTAIDz6I/4EHOCHLHBs51svZ2m0VGvKGmEpO8dbyW7SNNplqhmQwiSqr1No1LmYuEvFG2Ne/j2KjiCZrVNtV/o+X/g88qoeDAweZTLoC3Hqtw7ncFs0OPLM/jVfRKDkaPuH6aQ9XUyYKAgLQ2sVF+qcdtm1zMXORXC2HIiocGjx0y1iKW+HPTv0Zf3LyTwh7wxwcPIgsyjQ6Dea35vm9F36P//FL/yPT6e0FQ6cDzz8PMzOuXmpiwj1283l47jmcVovaviQFs45X8Lqp56KFKIr4BREPPpYtnWxnnf27EZVdYNo280ab+W6bjuMQk2X2qT4GrrMMiHqjRP1RtxUbvLEV39bbyII7nfde6Bpd3l55m4ubF5nJzLBeWcfBIR1Kkwwm6Q/38/jex5lKTd3W3/Bh4xMlKoVCAcuySKV2XthTqRQzMzO7vuZ//p//Z/7Vv/r0i4NeX3idleIKh4cO90gKQDseRTh4EOHdd1mYPcm9R59yD/piEVQVdfpeJg9OMtwtoBttZMWHtyaQCPj5tkdjNjtL2BdGFmUqrQo4bomv1CwxEO7Hcmz8nSZ9DrRkhY6s0KgVsVsl9OxFYuF+Jj0B9mheZM8oI7Ehjo0cY35rnscmHmMwNkhLb+HX/IihGAx/jCNuThvM7A6SAvDNc2+yUMgwEe+n2a0Q8kdQlJhbvu82KDaLpMNpBrQBMpUM1XZ1hyZFUzSa3eYH2rUBReOEL0hz4AAvlla4XFwlGkrhlxRGFA2p0yDfLPH41OO7rnDeWnqL5cIyk8lJ+gJ9CIIrqiw3y5xdO8tUcopio4hpmTdtUemmzunV01zYvEClXQEg4o1wYOAAj009xmsLr7FUWALcseOt+hZj8TGODLkl7eXCMkvFJSL+CGFvGMuxkEQJW/QyV1zjXO7HKJKG7Tj0j01T77b54ew7M1pofQAA/65JREFUrEQFvnD0N/DtQqCW9A7nug3ato1PFHEcyHWbLBkdjnuDpK5dhQWHwKtBYAaEGKiRbc1KydVzzOeA6nbooQmBAEwOwtgolIvgNKBYh+l7rooPRQX8/a7vxPAgGHF44AG3sqIorhYsdvV4EkURTbzRI+ZanBg7QalV4rlLz1Fuuk611XaVhdwCqqRycOAgmqKhyir5Rp7p1DT5Rp6YP0bIG+pVtYK+fib6BlnYmmexkOFgegwJh86VkT6nA4LfrR4K7jHjwPWBAT/1mM3M8l/e/C/MZGdoG20EBFLBFJ/f/3m+fuLryHewcNqqbfHDiz8k4oswEhvp/T7gCXBw4CBn1s/wg4s/uEpUFhfd4YHJyat2DKLoHlfFIsKZM9ixCZYaa0jlLO1uA0mSScWH6U8M49UCiEgU9bxLnN9jEdKwTb5ZKzCrtzFwkBwwgTfEGo/6wjzuj/SeG/FHeHzqcf7PN/7Pns3AFTiOw0ppheHYMF/Y/4VbvmdH7/Cnb/8pry+8TqPToNwuuxo0By5lLrFYWMSn+Hhz8U1++b5f5snpJ3ut0Y8Ln3jr507xO7/zO/zWb/1W79+1Wo3h4eFbvOKTwcXMRTRZ20FSABxRYO3QGEZjk2R+1fVTWV937ej374d9+8DjQQvEuXo5LRJ34CuHn2WuuMS5jXOsldZo6S1ivhizuVlkUUY3dVqCQMMxmRBlTEunabTJVNZwgLisMuiLYm+nN0uS6kbTe4IEtSDfufAdxuJjGLaB4zgEPUH29e/jwT0P7moA9tHB6f1XudXgdGaRZDCMKivY2w8JgkBAC1CxKrT1Ns1u071BCFfmna6irbd7GoIPghHFwy8PHWJEEHhz4TVq1QxeSaaK2zJ4aOKhnlnYtWh2myzmF/Gq3h3tEVEQiQfiZKoZVkurxIO795rB1U68PPdyryc93ucaQV0ZTT4+epyv3PMVNiob5Ot5uoZbtk0EE4S8IWzHZq281jseW90W5WYZwzIIeUJIYoCOkUOVZHL1EuV2k/FokITWYLZkktrq8OB1Q0dFU+e1SoZau4rPNmFb1ByXNXKWwZlOg8d84etGNB23XSMFXJLiOHB6HuptSEddwmJY0JahK4KhgM8P5Sy0aiCoIF+zvVbbfb6jgVAEOwAjIx/IjDDoCfKlg1+i3CrzvXPfY7W4SrVTpdpx22jVTpW4FEcS3WTqYtOtrOiWTqlZulpRk+LI6jBeZZm5/BoH+wcYEhq8ayWwqSM6gOTbNrAT6dg2siAQuzv508NqcZX/9YX/ldXSKuPxccK+MKZlslHZ4I/f+mN0U+fvPPJ3bnt7Z9fPUqgXODR06IbHRFEkFUrx7uq7VFtVwr4wzM66gvDdPKNiMfSLF8i/NsuMWiHtC+HTvOhGl8vLp9kqrjM6cT9BrxcVY7s1e2sN2vfqZc52mwzJKr7tc9WxbQq2yfPNMiFR4qj3qlfTrz3ya5zfPM87q+8Q1IKEfWG6Rpd8PU88EOcfPfmPCPvDN3s7ukaX//r2f+Uv3/lLAlqAtfIa1XaVxa1FdEvHp/jwqB4ODByg2CzyJyf/BAGBJ6af+FjJyidKVPr6+pAkiVwut+P3uVyO/v7dy2SapqFpH+dN8/3BsqyblvAtRebyVJJAdJIv3/vzLmM/edIVqe72mnYb4nFioQR7ZYXlwjKaopGOpPHIHjaqG6wUV5AEicGBA9gOPXt3jyjSdCwMQ6fcKLDabVBp18jJCproahDaepuLmYsIgsB43zhTSbe8V21XeXPpTUzb5PP7Pv/BQrhuB4IXlLQrspRcYpFvVOkYBlGvH1Wy8CleWqbT8//yKB7X30MU2apvockaAe0qGah36sii3Luxf1CogsjjQ4e5NzHBammVlt5CkRQGo4O9qQ/TMql13JZkyBMiV8uB4E7IXL/yAffGuFZc4ytHvnJTMe1GeYNzG+cYiY/gU69WixLBBH7Nz7mNc0wlp5hMTuLX/KwUV1AVlXfW3uG4cByP7GGluELH6JCpZtBN3W2ByAqblU1KrTq2JRIWIKRazOcukvbvw+PZQ18sxsXsEnuSB7Edm3w9z2J+kR+XVsnJXsKWjl/1gQABLcj+9D4GIoNsmDo502BMveZvEsMghcEuu1NFxRrMZiAeAr8Edtc9B6wJ0AXIVWFjyyUnHgf8IejYoDdhLQuFqmtEKDig2LC/363EfED4NX9vxe33+IkH4yxsLWDbNgtbC9QCNSRJIh1OY1gGsiRjOzaWbV3diCCB5zCqN0/LnAd9lkFWWbWnyAghknIfiucIyEO0bYu8ZbJH9RD/mFerHwi2DVunYO1l6FbAn4Lhz0N834eiY3v+0vMsFZY4Ony0d27IksxofBRZknn+0vM8tf8phmJDt7W9ttEGgZueZx7ZQ7VTpW20CRN2pxx3iQrpGl2Wi8uszb/CuXyezmSMggN9qo9oIIbPH2WjuImxep5H904TlqM36AuvR8boMqO3SEpXSQqAIIokRJVlvcO7nQb3aP7egqY/0s+/+/q/4xuvfYMfzf6ISquCLMo8te8pvn7f13li+olbvufJ5ZO8Ov8qiqTQ0BusllYBV0Ru2zambZJv5GnqTYajw9iOzcvzLzMSH/lY20Cf6BmhqionTpzg+eef5+d//ucBtxf5/PPP85u/+Zuf5K59YEynp3l98XVs275hlWzbNq1ui4k9x9yeZyLhJhEXCu5/bz9nqbDEcnae4MYWnq/8HIcch7eW3mKtvMa+/n29k21/ej/VdpWV4gqC5keIDuI4DpZlMrt+hsvrZ0AQyJbW8GgBYuF+Qt4w60U3GDGoBam0KgyEBzi3cQ7TMplIThDxRZBFmZnMDAcHDu7wlfhIIAiuwZi+CGYe5ITrh+ILuNUjTScRGmC+3O3ZwTuOgyqqjPeNs5BfYDQ+iiRKtPRWTxR239h9DEVv70K2A47ltqKclruSl/p749E+zce+9E7fEtu2uZS5xIXNCxQaBQACWoBGt8FScQnd0FktrhLzxRBFkbbRRhREumaXgBbYkf90PZaLy9i2vYOkXIFP9WHbds8+//TaaTpGB8dxaLQb/MU7f4EsyWxVt9ypFcei0qogCiKFRoGgFsS2bXQLOrYPSxqmbosU7GkGPdM0myuc33iTjfIGK6UVumYXAYFidBjFG0awbYIe15Ol0qpwbv2cqw3yhKhd774paqAehNYLYIdgvQCGDcNp2FwDvwhtyW3tKBKIAiyswmOTEGrDnjRcykG9CY0WhPwQ9EG9AisFSJRdzVf45qvI24FhGqyX1pElGb/qJ+KPkK1msRzLbcGtnUZTNBzH9euptWuEfWE0+bpFlBigKU4z0nc/hPwEzHVO2AHOitNsiSPYYghMA00QmVA93KMFED+lHkU3QG/Bm/8vWHkO9Lr7nTnAzJ/A3q/Bsd/cOY5+p5s3dd5cfpO+QN+uxKI/1M+59XOcXj9920Ql7ncrYR29g0e9UbBcaVfwqT50U3erKqGQO+m4ja7RZaW4ymsLr5KpbBLO5DDiA8QUmWZtlUyrjN0/RSgQZzDSh1VdQW/2MRK9sYJzPdZMnaZt0X+TVlZMksmYOhXbJCZeban2R/r57Wd/m3/8xD8m18gR0AI7NGQ3Q71T5+LmRUrNEplqhlKr5C7sBJmW0cJxHLpmt6fPqnvrVNtV/JqfM2tnfnqICsBv/dZv8Xf+zt/h3nvv5f777+d3f/d3aTabvSmgzyoe2vMQ3z//fWZzs0ynpntkxbZt5rbmSIfTPLjnQffJoRAcPw4//jF0u+S9Iv+/0/+NzNosgUqD5ZjKmTcWGFv/LlOpKZKhJIv5RRrdBoqkEPVFSYVSLBeWyZVW6Qv1k21VOTvzAouZS3g1HwhCb7rHWy8wX80R9UeJB+LMZGbwaT7SkTRds8vJ5ZMEtAD9kX4CngDrlXXXUO6jJioAyhh4H4POm6DPEJENhoKwUGhT6MSIhQ8w4OTIVrOuuVuzQNwXJxFMMJmcJOwNU26WcXCIB+I8NvUY+9P77zwo0NiA9htgbrjtCESQE+A5AeqNq0XHcXhz6U1eX3gdr+olHohTa9d4cfZF1svrGJbBUGyIYrPIbHYWj+Ih6A3iOA44cGTkCHF/fPd9wb2o3Kr9pikaFzYv0DW7pMPpnjnccGyYH1z4AW8vv917v/H4OM1uEwGBrtHFsq1e5s9IfIytZouwL4zhaCzmF3l39V1aeqtXfVMllbmtOWLREeKBPuxWmbXyOh7VS8wfJ7vdykoOHGLXT91z2K2odM5BbRHEBiS80AXyETC9IBTActx2j+aH8SFQhyDswNk8rGRgOOl+D40GVEpw4DDE+twJjS9/+QOt6DcqG1TaFR6eeJiZ7AxbtS1UWWUmM0Oj2+jpuFaKKyzmF6l1ahwbOXaDwaCrjZKZGnoKwu6UXwx41HHImzpNx+65mEZF+aOvWn6YePvfwfw3XQ+c8BgguOLmxqab/uvtgwN/+31vvq236erdHVNZ10ISJQRBoK23b7oNq5c55X6ux0eOMxYfY7GwyP70/h2fd7lRZj43zz3D9/DfTv83NFnjoCBxpFog2O2nIzquRixzga3aFgOmypZXZsVrYzR1wqEQul7DzF9iyHcAQbKZs9qEnAADnj032cOrcHAQcCsou0EQwN72x9oNIX+IkP/2HcNLzRKzuVkanUaveiIg0DJbtI02lm2hiAq246bB5+t5JhITtPQWZ9bP8IvHf/F9BbC+H3ziROVXfuVXyOfz/It/8S/IZrMcPXqU733vezcIbD9rGIoN8WuP/hp/+PIfcnrtdC9/pdwqkwgk+NWHfpWR+FUxF0ePgqbROvkG3//+H1AurhAMRakdOUBtJIZqVnh98XXeWn6LRyYeQRAEZEnGst0WU9wXZyAywEJ+ga12lQvZy9QbBVKRAfrjw2yVMyjhAOV6hnqrhKF38agebNumY3QAWC+vYzs2tU4NwzZ4cu+TDMeGEQUR86a5FB8yBAG8R0EZBGOZpFriyIhM2ZqnoYtkqgU0WSPqj5Kv54l4Ijxz+Bm+fM+XGesbQ5EUau1az/DtffVRzRw0v++OjUqDrvmZY27//jn3OdpOM7ZM1bVXT4aShL1uH30+P48gCBwYOMBMdoZCvUBAC+BVvRimwWh8lIAWoNlp4lW8vL3yNk/tf2rXXQp5Q3SN7q6PgXtDbHQaDEYHdzjYypJM0BvkyNAREECVVQqNAmFfGGl7iqdjdtAkjT5/H2FvmEK9QL1Tx7AMFvOLdK0ug9FBmt0mQ9EhGt2GS1rK67TiI0QUL12jQ7FRJOqNEvKG2GqUiJo6Md8ulQ1BBd+ToIxD+LsuafGNwp6joHVhfQP0EggNiATgxL1w9L9zV+znvgk+HfYNQKMLjSYIJhw8DPd83g0BXF3dUZ18P2h2m9iOzVjfGEFvkM3yJmc3ztLoNugYHYLeIAE1QNAToN3ZQqZGrvgu8xth0tH9SHKMcrtKs9vkxMgJRuOjO7YvCwLpj1X39SGjvgarPwQ17IqZr0BUIDQCpcsw/99g6pfgJkTjveBX/UR8ETK1zK6TdLqpu3bxu0RWlCyDVaNL1nCTz+KywrCs0a9o/K37/xb/8cX/yJm1M6TDaVRFJVvJcil7iaAWZCAy0Lt2nJQ26Gg17r94lqxikGmuo+o2I22RkCBzajyJHI+h6y3o2ERDA2yUVlkoF0jGhvApKRK+/TQcB8VxblktS0oKmiDSsE0Cu+T8VCyTfkklcgcZQLeCYRpkqhn6gn1sNbaotWrutd40cRwHWZCxsHBst2pdbpUxLZOAGnB1cGYXr3pjW+yjwCdOVAB+8zd/8zPf6tkNj0w+wlBkiFfmX+H1xdfJVXNEPBHigTjvrryLR/ZwYvSEO+Wx7VHy3fo5/tO5OoPTB5AiUdpeV8SVIEFbb3N+8zyrxVUenrrq0qmbOrlaDlVWkUUZu9tAtS001YcpiqyX1rAdm1Sonz7VQ668ji2Z6KZOf6gfr+p12w+eAIqk9G72p9dPYzs2juPs0H18LJATICcQgIcPPkhLfI4Lmxdo623aRhtFUphKTvG5fZ/jyeknd5SGb0c4W6gXWMwvkq/nkSWZkfgIWniAvCCgtM+QtGoMqONXVwyCvE2eNqBzyk3xFa6WX5cKS7SNNiNel3wWGgWKjSLJYBJZkon5YqyV1jAsw622WDWq7SoexcPe9F6GY8PMb81zbOTYrhfe8fg47668S71TvyH4sNFpoG+7CMcDO6syHaNDx+jQH+mn3CqzL7WPN5beQBZk2kYbn+rraWxwoNFtuCI6zUfX6LJR2WAgPEDUH6XWqbntto6DIik4jQLdeoFuZBDV6NDo1LFsE0cQqcsqCVEieTPvBUF2P8O9vwynW1DvQtiAPV4YPwGmxx0DzeXgqa+4+SgA/Y9D+BIM225rzpEhOgLRPa5pnOO4jrXtm6+ybweSKOE4DrZjE/fHifliZKtZ+kP9KLJCrVPDtk28FNgTF4gPDbJYqiAYizSqBWx5mGj4OI9OPsr+9P6fvMDCzTdcZ9XYbuV/AXwJqC5DeRaSR97XW8iyzON7H+cPX/lDWnrrhrbncmGZdCTNidETO36/YXR5p1On5dgEBQkBWNW7rBldDmo+7h2/l/9B+x/4q4vPc7a0RrNbZ6O0hkfxsCe5h7XSGvl6nsHIINP901xwQMhbyJfXSDUNzFqVZjjE0uQoRa1Ku57Hr/mp1MtgWrTrHVbFEptlnXAwwaqs0WpVSMoq92h+gjdZPI3KGuOKxiW9xagsolxTrajZJroDR70B5A+pinElR8yn+RAQdvowCbgmkg7Y2HQttz1tORZto81AZIBGt/HTRVQ+C7Btm1PLp3ht4TU2qhv4VT/3jd3Hw5MP3zJPZrRvlK7ZZbW0Sr1dx7RNio0ia+U1Xl94nSemn+DXH/v1Xln/xcVX2QpIJNI7nV+dbf8UURBZq6z1/g3uKllTNE4tn+LQ4CHC3jCqpFJslvF5Q6yVVuh06ihaCFmS0GR3tDIVSqFKKh7FgyiIbhsCtwR5ZRtvr7zNo5OP3rAi/DgR9oV59vCz7E/vZ35rnrbRJu6PM5WaYjg6fMflx4ubF3ll7hVqnRpe1Usbh282q5jhPOFglJRdRRVGGLQknvRaxK/dvpwEYwXMDChXP5Nqu4pXuXrSNrpuOfWK+LTerWPYBsOxYQzLQJM1BEHgxMgJ0pE0kihxOXe551p7PQYiAxwZPsLJ5ZOEvKHec0rNErV2jYMDB12N0nXDrZIgIQoiluOKPOOBOEeHj2LbNo1ug3LLHb/dm9hL02iyXFh29UqRAdbKawQ8AY4MH0E3dVYKK4DrTSNLMralw9ZllECcmieAAFQklbrRpl/SeCAQ65Xcd4Vjgf8yTOfh9RmoaBDzguyHZgJqAbjnCIyNXX1NdBBSB8GnQcAHogrXXvjbbdA09+cDYCAy0GsjxgNxTMt0PTxkhXQ4jSIpjIYEDsVNBLkPBxmlYTMYGePZ/cexrS0C0QPIvsMfaD8+tbAMXEHKTQiYIAMWWDevAt4Ontr3FGfWz3By6SQxf4xYIEbX7JIpZwh4Anz93q/vuP62bIuznQa24zB8jV4oLEHNMrnUbeMVBFaC/UjHfp5UrcDM+e9iFFeZiA4xGk4iCyLNbpO5/Bw2NnsSezgprSGFx5j2pjm3/g4VTSQcipPoaFTaVYr1IluNLfe6rIWwJBW7U0VQPdQzFxmdfIx1o4tu2zzoC+0aViiKIs8E47SqNitmF1UQUASBtu0gCXC/N8C9HyCd/XoEPAGSoSSrxdVeu7rQKKApGmbHxMJt/XgVr1u198epd+ukQ2n6w/03TLR+lLhLVG4Dpmnyjde+wfcvfB/d1PFr/p6W4+W5l/knn/snNxVztbotvnfhe1zMXMQjexgIDLiOgY5DoVHgBxd+QCqU4mv3fg3HcWh2m7seALqpU2lVkASJYqPIyeWTDEWG6Av2IYsyuWqOrtllLD5GqVVCUzREAWyzg2jb1FoVllnCp/pcfYIggO22onyqj3Q4TaPbQNAFOnqHeCBOq9ui1W31Jkk+SXhVLwcGDnBg4MAH2s5mZZOXLr+EJEpM909jAm9rAVqiitMsITsNxkJN6kKIWVOi3YZf8Nn4r9xwBQWwt3UrV+FX/eiWvuN3tXaNQqNAS29RbpXpGB3KzTKpcMoNA9T8DMWGeisbYft/u0EURR6ZfISQN8SFzQtsVjYB1/DtvrH72Jvcy1+++5c7nHnBnV7pC/QxvzVPIpgg6AnSH+rHo3jwKm4lzbRNGt0GPs3H0eGjdM0uP3f052h2myzkFxiIDFBullFllY7Rwa/5iXgjLBWWiPqj9FU3aXUaJPvGSHTrKJUNntn7BJH3am10zro5OCcOuTb7F5YhX3czf/wVePDn4f4nd46GRiJugvfFixCJ36hDyWRcYvMB2j7gttruGbqHV+Ze6f37ipan0qoQ8niYjBpIchgL93yWRBnD1gn5+8EwwZwF59D2TfsnDJEJUHzupI93F21Vp+T65ETen+tqrpZz29jVLQ6kD+CVvVzeukyumkORFE6MneBLB7/EA3se2Pk6U6dqWwztUskLSTJFvc2f1gqUTJOIJBFolRCaeRRfkKwko+odJlUPfs2PJEqsldYYig5hWia6IpDxOUipNNX8IkEnStATZDAyyFp5ja7ZpdCu0lZ0YpLEWGIPE6l9LGVmSMdGGUpOsmHqZEydPTepRCRklV+NpjjXaTLTbdGyLaYUlUMeP9Oq90OrpoDrwXR06CjVZpXNyiYBLUCpWUKTNSSfhGmbyMiuq7rj6uD8qjsBN5mc/FACX28XP4Fn0IeP52ee59vnvk1/qH9HlknX7HJ2/SzfeO0b/PNn//muK/vV0iozmRkkQSIVSvWqIIIgkAgmqHVq/Hj2x3zp0JcIeoLuuKNp7JgWurKaq7ar2I5NMpBEN3XOrJ8h5A0xEBmgbbQZDA+SDCXRbZ1au4Ztu94Ziqjg1/w4OMjbgj3DNNioblDv1PGqXmL+GGElTLFRdIWe271a0zLf38TMpxRzuTmaepO9KTdCPS+q5ESNqG0he/xU2yVaPpug0kGRFNYsmTnD5uiV657ddsmKsLMMPRof5d21d2nrbbyqF9u2yday+FU/IU/I1YeIKl2zS66WoySVeHL6yd7xUGlVCHlDvbC/3SBLrrPqwYGDlFtlwI0JuGJtfWDgAC/MvEDAE+i16gRBIBVKcWHzAqqkYloma+U1ys0yG5UNJEliMDyIpmgMhAfQFI3Dg4d5ev/TZGtZNiubtPQWEV+E/lA/K8UVUqEU/eF+crUcXaPL0tYckiBt38xNHh06wsH3ClS0O6Cfc8eV5RgcjLnpyeWGO/Lqq0JIBO26S5QgwLFjLiGZn3eNt3w+t5KSybijySdO3FZWynvhSkvh3Po5ZjIzmLZJpVWhP9zPRCxMRF2jg6u/aOhdFFFkMLz9/UkxsApgV0G6uUj6M4vUcTeTJvsmqEFXG3QFegP0Kuz5yu4k5hZo621+cOEHvLX8Fh2jw0DYjX6QJInH9z7O4cHDJINJBiIDu15vG7aFBDfVgpQtk4Vum0MePz5RYqW4TMAbotOqYNsWWVMnKStEJBGP4qHYKLJZ2aTRbVBtVd1rrhYiX89TaVeYSk4hiAKyKKPKKpbioy+5h2F/HEcUWSmt4hFFVrfmGE1N4REE1o3uTYkKQECUecgX5qHd9F0fIjRF48jwEea25txpH0mma3SptWsIgkCtW8OrePGqXiK+CIcGD/Xa2YeHDn+swu+7ROU9YJomL86+2DPPuhaarDHeN875jfNcylzi4ODBG15fbpUpNov0h/t3/WL7An3k6jk2yhvsS+/jZw7/DD+8+EOy1SwDUTeAsNx2Q9QkQUKSJI6PHmckPkKlWSFXzxHzx9xKSadJyBtiWBgmU8ngOA66oaN6VPyqH0VSqLQqWI6FR/FQbpaxsfE4HvL1PJZjkQ6neXr/0wxEBrAdm0wls+tI7GcRjuOwWlol7L16AchLCjag4YAgYzgiFStKSN5CEfwoOMwZgktUHAesTZBHQN45ATUUHWJ//37OrJ8hFUpR69bQFI2W3sIwDfoj/YS8IRa2FmgbbUzb7Ile23qbbC3LA+MPuCZT74ErbbvrcXjwsHsx3ThDxs7gVb10jA6CIPDVe76K5Vj8aPZHrJfXifqj+D3uqtGreCk0CnSMDr9w/Bf43L7PocgKg5FB9qX3cWbN/Zv2p/djOzaLhUXXhj8+StATRLd0xuPjHBo6xHRyL+OJPe8tYrYKbjK2fE1LUVUgta0vsn1g5cEqu3qla9HfD888A6dOuWnkm5tuq2dszCUpH5IBpCzJDEYG2ahskK1lSUfSbFQ26BgdNMndJ8dxaOhdthpVhsIh9vW/93THTwQkBY7/9/DK/xPKl0H2uj9Gw233pB+AI//ojjY5l5vj22e+zctzLyPLMgE1QMfoMBofZSIxwXJxmeXCMvcM3XPTdq+EcNOpGHD1K6IAvu3Wi2ka+DxhgoE4pcomdiBO0TKIXGO6d2b1DD7Nx0RyAsM2EByBZDDJemWdmexMzwU6FUqxZ+wEUqifwLbpZKvbotgskatsbgtUBXTH2XXfPgkcHjxMqXV1PPngwEEu5y4jCAL9kX5EQaStt0kGkmzVtxiMDPK1E1/72KUAd4nKe6DQLLBeXndTendB1B9lpbjCSnFlV6IiCAKWZd20n2fZFoqg9IyiHp54mK8e+Sp/durPqGfqRH1RcvUcjU4D0zEZCg+RCqbQZI1UOEXAE6CpNzFNE5/mI+QJuXP04TSz2Vlivhhdq0vbaCOLMkPRIZLBJC29haqoDEWHWCms9G6aVwzBRmIjLBWWGImPvGdWRNO2yJg6m0YXw3GISwqDikqfpHzqxy11QUC8bhebYj+6JKBZWbxOFN2WwGqCteWujr0PXrVw34YsyTwx/QQ+zcf59fNc2rxE2BOmI7lEQVVUTNvEo3gwLAMBgY3yBm29jSiKHBk6ckMZ+06hyApPTD/BRHKCpcISlXaFoBZkT2IPw7Fh1svrrJXXmEhMEPKGiAfi7pRX2zWny1az9AX6egI5URR5Yu8TeBQPs9lZtupbBDwB9vXvw6f62Ne/j8HoIKOREGExh2AuAm9CZw3UKZCHbzEibLvJwbsPMLtmaY4F3OSink7Dz/6sO91zRZeSSHwolZQrmMvN8cLMCzS7TUbjo+zp24Nt27y1/Bavdqsc7rMQhQ4ICv0BD09Nn7h6nbBKIPe5FaOfVKSOwuf/3zD7Z7D5qktSIhMw8jRM/w3wRm57U2ulNdfcrbhE2BdmKDrkksBug5nMDLLomrytllfZqGzc9EYZlxUkHXTHRr3uHL2SzRW65locDMTYyObpi43SbFUp1go0gwlQr4rJr7Rd9yT3kAgkuJy7TKVdIRVKUWwUiflipMNpHt/7OC1ZI2sZSLaJJqn4NB9b1Q2aettNsLcdUuqnR1ityAqfm/4cftXPX5z6C7bqW0ynpumYHVRFxTBdPV1foI+h2BBfO/G1nWG6HxPuEpX3wBUVtH1Lns5Nb8jj8XFC3hClRglfbGdl4spNoj/c35vkkESJ3372txmKDfFXZ/7KDQzUm/SH+rl37F6G4kOsFFdoVBqosnsg5eo5Prf3c0iSxHppnb5gHxOJCea35tFNnXw939MyTCQmmNuao9apoZs6433jlJolFvILPQv6zcomhUaBh/Y8xIN7HrylWLVkGZxq1ylaBl5BRBIELhsGS0aHA5qXvarvU0NWBEFgND7KW0tv9SoSfsfCwvUmsB0TURDR1ChNNYVprtPW80wJNfem6TkO2iFXULsLPIqHx6YeYyo5RaVdwSN7SAaTNI0mpUYJ0za5Z/AePIqH8xvnGY2Psi+9j9HYKIPRwQ9lMkQURUbiIztH37dRbVcJaIEbEpqvVMwkUWKxsMgjk4/09kVTNB7f+zj3DN3jJs06DlFf9GoImrHmjmzrhe2bsgTdM6BfAs9D7me22/cvRkAMgV25IdsJcCspYth9zs0gCB9Yi3IztPV2L0RyrG+s1zJ95vAzDMeGeWPpDVQVDsUaRPyj7E3vZzI56S5IrKqbW6X9hOpTrkV0Eh78HTBaYJtuVeUOIwAcx+H8xvleDMaVa6EgCAQ9QWzHZrmwzHBsGNMyKTVLNyUqfZLCoOxhxeiQlBQ829cuw7HJmgZRUca45lqe6ttDJncZUZBI9I1TyF4ik59HLwiIoohP9TGZmGRP0r05D0YHSQQTFBoFdFNns7JJyBvCdExWCis0bYOaP44giviAkBYE28bjDdGyXeI9dJ0pYKPTYK285lbqZI2h6NAOi4GPGpIo8dDEQ4zFxzi3cY7FwiKblU3KjTIBT4DRvlEODx5mOjW9a/jhx4Gf8LPog6Mv0MeexB7ObZwjEbjxophv5Al6gjdN5k2FUzy+93H+/NSf49N8RHyRnhtpvp5HFEQODx1mIDLQe40syfzdR/4uf/uBv82plVP89bm/Zjo1TdgXxnEcBsIDZGuu4ZkkSKTCKR6dfoK3Ni7wvYvfp9Vp4Fe9NFtVwp4AD+x5gCPDR4j5Y7S6LRrdBqIgIgmuWAxcjUWz20Q3dQpN10cjFU7t2K/rYToOZ9sNKpbFkKz1+sIxoKx3+FFmliWzQ0QQifqjjMZHb2re9HFhb2ovlzKX2Kxskg6n6bd0lmQvdcBsVYj6Yu7FURDJiePU1X6GAwHwhEG8PUFxMphkun+aTCVDyBciRGhHVaqtt5lMTfLs4WfdseCPCVcEuzeDKIg4toNt2zeQpogvcqN4zu5A6yWw66BMX0NIkmAVofO6S+qUXVoxUsitunTeAjGwPeptQ7fh3uSdHAS/6HrYfAI4t36Ok8sne9NYfs3PSHSEdCTNkeEj+FQfYa+PZyeDhMR1PIoCdhasNohe8D4A6nvodH6SoLz/9nCj22CttEYilGCtvHbD40FPkGw164awwi2PYUkQOOrxIwmwYejkLVf0LuGmevcHFL7fKNOxLTyiRCwyyMjgYd65+EPKnTqK6ifhNeg2S3glL8Ox4RvOUVVWe9dFSZSot+suaWlsEvaGiWkhWt4QtW6TXOUyydgwWmyEkm2yT/P1gjqvELSTSycptUo9UX3EG+HE2AmODB352AzV4Goqeq1dw7AMvIoXWZIRBfFjDyG8HneJyntAFEWe2vcUFzYvsF5e3yEsbXQarBXX+Ny+z93STvjrJ75OqVXi9YXXyVazaIo7mqpJGveN38fT+5/e9UBQZIX7x+9ns7rJRnmDsC/sjokF4j2/jI3yBm3b4i8W32S9tkW8/yCeTpVyPUvVsVAsvUdSYLvy40CpXSLmjVFulhmODSOLMqZtYpgGW/UtDg0eYqW4QqG+e4Q4wJapk7dMUrKyQ7zW7NQ4P/cKl7YWmRFgWPW4BCsywBN7n/hYb87Xoz/cz5PTT/Ly3MvMZGfwKl6C3ihrwQRef5JIOEXDtqnYOqYDD3hjTHlid9RSEEWRA+kDrBRWbvA9sWyLtfIak4nJ24pf/zAR9oaRJKkXP3A9qu0qk8nJm6Y33wBjxY0YUCZurJpIcde0TZ/bnag4NliD0AqA+Zb7+k4VjLxbvRLH3TyfVOsD3QTfD7LVLN+/8H2WCku9akqpWWKrtsVYY4xDg4d651Ms8TVkJwv6MjhNtwqkjIOc/lCybn4aYNkWlmOhSirJYJL5/PwOHdkV64SW3kKV1FsKzgG8osR9niCTqknZMnGAgCiRkBQMx2HF6HCx2yYiSkRFGU8ohRgbxtuuE+nW2OPrI903TSzSz7uZ88zn55lI7j69VGwWyVazZKtZfKqPttFGL68gNv14vCF88TEkfx8j/jgPekMMXrOgm8vN8aOZH+HVvEylphAFEduxKTaK/Pjyj9Fk7QNPOb4ffJzVnNvFXaJyG3h06lG2Glt8891vcmrlFF7Fi27pyKLMQxMP8fce+Xu3fL3f4+efPPlPeGjPQ7y59Cb1tnvzOj5ynP0D+28Q6V6BaZq8sfQGr8+/zsmVk/gUH8fHjnNw4CA+1UehXqCpt1izLIp6i73JCar1HGazS0jSIDxAtrTKc7Mv8vjkw/hVf88wTRZkVzfhmD39jCzKtO02IW/I7QeXVlkrr92UqDRsCxtnh1eGaZm8M/cKq1vzDEQHUWUPk54AjmOzWlzluYvP8dWjX/1Q0ozfE44NnbL7/2oQZHd1Pt0/TTKYZKmwRL6e56Ck0AqnyahB8o5Fy7EZkDWOegOc0G5usNTcnhKoWCaSIJCQFJKygiKITKWm2Kpv8e7qu2zVtnqi00anwXBsmEenHv1YV0vgCn4HI4OsllbZk9hz1dwJeqvVff37bvLqXWC72hZulggrhsDcvPH3zRxsvAr1dbBa0MhDe96d2EkeAnEEdJXW6it0Kpto41/Cf4t4gQ8Tlm3x6vyr1Do1+gJ9RH3ucRr0BOmaXZYKS8T8Mdfx1xNEklQQRnd46tzFncGv+gl7w1TbVQajrni51CwR9UVdXYfRQRTdXKr7x+4nGUhimibyTTJxwF2QxSTlhiRqCfillsX4zBzrlQK61sTnWeWxPodhI8TQvERguYTZWkVXNnDCAq9rFYqNYm9x2Oq2yNVzXNy46Ia5igJe1dubqhQRSfsipMNpakaHan6eo9KXGLmmmmzZFmfWzyBJEv2hq8J8URBJBBPuVOfaGaaSU7e/cPgJxl2ichsQRZGvnfgaR4eO8sbiG2SqrtnQ0eGjnBg9sevq9HpoisZDEw9x//j9dIwOsijfMrula3T5/R//Pi/OvojjOAS1IJvVTf7inb/gtfnXeGTyEcbiYwz27+P82mlGIwOs52bZLK4gSwoe1UtEFKhoPtarOcqNMo7PQZVVvnzPl1koLHApcwnfNavVtt6m2q72xJKSIN3Stt0tzuwUOxaqm2wWl0nHR9BF2c2uAERRYqxvjNncLAv5Be713/uen9n7huNAZR7y56CVvUpUYvshcQ/IHqL+6A1kybZtqrbl2u+L8i09CzaMLmc7Daq2hSIIWI7DZaBfVjnuDRIQJR6dfJSh6BCXc5fJ190W4cMTD7MnsecGd9mPA7LkOn0+d/E5LucuE9SCyJLcS5h+YPyBHSnTK3qb050GHcchIsrc7wsQvXYMFYGbil0BsG7UaHTKsPIDaBcgsF2drG24FZS2BI0EFV+cs8UV5gubdC6fQl29zMT4ExweOvyR98g3K5usldc4kD7Aye5JGt1Gb9RbkzU0WWO5uEwimODe0Xs/NfqrzzIUWeHgwEF+cOEHxPwxDg0e4uLmRXd8XpTI1XKkQilCnhDn1s/xrTPfAuBg+iBP7HuCYyPHbu+NTBNeew3/uXM81qrRsZZpNpbI6TmEsSSBho6+WmdO8LKmK8hdg9i8wYTa4bzvVQ7sf4haq8abS2+yXlknV8sR8brBrR7F40ZjWAYto8VaaQ0HxzVH6xvHd13Lu9gokqvlbjqkkQgmWC+vk2/kb9l+/2nBXaJyB5hMTTKZ2l2LcruQROm2zNO+c+47PH/xeUbiI70b6gHzQC/UrtFp8Mv3/TKv5Jcwl0/S6dbZLK4Q9kdRZA3JtIiVdA5mFIxChil7nSee+SKBiX2oqoeLmxdZK62xUlyhbbSxHRtN1phMTDKVmnLTlx3rlhbJEdGtHnRtG237pl6uF7BsC1XWqJkGw9ttLnBXOSFPiMX8IveOfYREpXgBVl9wJ3O8fe7NsluB9RehXYTRp3b6PmxDFEWit1HlKFsG73bqWI7DsKz2/j7TcciYOqc7dR7yhpFEkT2JPe9PJd/Ku2SrtgIIEBqFyCT4PtiNOhVK8ZUjX2F+a94VW1s6o7FR9vbvZSQ2giAI6JbFf6nmONlp0HQsBAcQBL7fLPFsIMbTgW3xq5xyPWXsbV3GtXAct+Li2WlvTmkWmlmI7nW/n/o6mE0IDoHepFmc5bmNNRabNZK+MH3+OO12nlMrb7NeWedLB79EMrT7xf3DQK1dw7Is+oJ9jMZHmc3OYts2QU8QQRCQJInl4jIHBw5+rOmxP+nYn95Ptprl/MZ5PIqH/f37ydQy5Ko5jg0fI+wL8+bim3SNLrHt4++F2Rd4c/lNfvXBX+Vn7/nZ936Ts2fhrbfcibFkGY+uY9vTdGcNUs/n6aoOa1MmXrFD1Byj4wRp6l0GL8/iW8rxtvdtTi6fpKW3XFM0QSLsC9M22nTMDj7Nx1h8DN3S3dDCyAAPjD/Aemn9Bm2e7dhYtnVT7ceVHDfbvvUQx08L7hKVTyE6eocXL7+IR/Xg4JCtZpFEqTd2rEoq2ZrbF5UkBQGBbHEFSZRRZA1FN9l3YY30RpGWpdMVJAKX5rHbf4H6yOfh0Uc5MHCAv/vQ3+WPT/4xEV8Ev+on5o8R8UUQBMENrvOEGYndOD1yBXFJZlDWWDY69AsKqiD2xqwrlolHFOm7rtr0kQcc6nXIvOlOH1wblianQIu4JCY8BvH3IXQ0DDBN1m2dpm0zfF1FTBYE+mWFrKGTVw36b6PStivKc7D2InSrbiUIYGMNCudh5HPv2+3zCkLeEMdHj3N89Piuj/9JdYsft2sEBZFRSUUSJQzbImsZ/HmtgF+UXDMqecDVp+iXXD+UK2TFMcFcBSnp5vlcgW26RMUTuzre3XPzFUEJkMnNUNW97E0eQBJEMAS8tk00OcFcYYmTyyd59vCzH1klQxIlHBwcx2G6fxpJlFgprrBZdVtYuqmTDqX54oEvfip7+Z9VqLLK5/d9npH4CJcyl6i2quxP7+fnj/48juPwu8//Lh7Zs2NibSg6xFJ+iT9960/Z179vVx2J4zgYONDpoJ47B+Ew+IH2BogxNElD8/mw6mu0VZuWESYRhKjYJNMNojsW3oEh/NUWua0VUsEU44lxio0ihUYBn+Zzc58s203KFtzEcp/qQ5M06p06EV+E4dgwjU6DfD2Pg4MmawS1ILV27YZ8LoBqq0rQE/xEKq+fRtwlKp9C5Go5N7XW6PYcSB3HPbgTwUTPsnm1tMr46L34vWEWMxeIbPfxxxayDK4VKCRCbHarDIYS1D0hmmEffW+/DdEoHDnC4aHD5Bt5zm6cJeAJ4NNcMVihUcCwDB6fevyWWhJREDji8eMAG2YHy4GW6qNimcQchwnVR+C66ZFap9Zzhf1IUFt12wvRXVa7subqVEozd0ZUKhW4dAkuXwbDwJGhf3oKYXofjmfnSkkRREygapnvi6g4rSK1ub+iUM1SFr0oUodkMEkiPIHcysLaj8ETB0/kjrd9O8jpOie7dXyCsINkKqLEsCixpHd4sVlxiYoggf9JtweoL4C5TToEEaR+8D0B0jXHj226JEa8pufeW1HatEyDfLtBwt+H0CMyBsheBFFiIDzAanGVYqP4kbWA0uG0m/HTKhPzx5jun2Y4NkylVcF2bLLVLE/sfaJnxngXHx4UWWF/ej/7+vdh2RaiICKKIt949RtUWpVdWzxjfWO8u/Yubyy8sYOoONvVzRWjS8ky0DYzTGY3CU9MELULYHdB6UMCkmqUKiZGp0vQjKHbIh6xQUev0+i2icYHWa+s0Kw3SQ3vJeKLUOvUeoQj7o+7eV6dOtma60XUNbuUWiUS7QQTyQn+6NU/Yj4/7/pmSQphb9gN/cNdOCjXaGkMyyBXy3Hf2H23ZQD504C7ROVTiHwjz1ZtyzV1C6UQRVf13jE7rJfXewe4gMCIJ8jR0eOcv/wSQrNE2JJIb5aoBjXyRh1N0RgJ9WPoLQgEQZHh/Hk4cABFUXhy+kli/hgXMxfJVDKIgkgylHTn5q/z29gNXlHifm+QguWhaBpMpKZgaxajWSB63UmWr+fxKt6bjnJ/KDDb7o1TuEkLR/FDt+bqVm72nGsxMwPf/jbk8zAwgBOLIRe3SLzwI9jMUv3ckzeQlfe71ncch0sz36Wy8ApZKeyGg9kmS8UlBsKD3DN4CE9jBWrL4Dm688WG4VrId7uu+Vk6vTMj5zZxTm9Qt21GbuKFEZNk1owua3qbYdXrjhb7nwFt0zXEcyyQIq57r3idBktSQQ1Dews82wTG07f9ndTpWmBYJqoaxAB3W2az1ybya34y1QwtvXXHf9ftIuwLc3DwIK/Nv4YgCES8EXyqD1VSWS+vMxwb3tXY8S4+PAiCsKMlsphfxK/u3i4XBAGf6mO5uLzj93N6m3PdJjgQkERkHCq2ScboMCF1ubZ5GA32YfhC1MsZau06umhgWW2WCg38nhhtvU0JHVX29loxV3RLlm3h1/y0jTaS4CZub9W3KDQK3Dd+H6qs8p2z32G1tOrqwdp1DMvoVeNkSabcLDOdnnaHHYw2jW6DieQEJ8aua5v+FOMuUfkUotQoEdACtI12bzJEEAS8ihfHcVgqLDEUGWJvai+aKPIL4w8wv/8LvHLxu9RLK3SLW2wkg3jVINOxEfyygo6XmD8KfgVyObdKkEigKe6I9OGhw72Mh5g/dkfmY5IgkJJV1x/A46d//xd4/tLzzOZmCXlCiILoJhUrXh6eePijHU+WNFcf4Ti7j4fqLbci8V4kpdmEV16Bv/xL2NiAvj5oNBAGBvDuGWXDjDA4M4vR30/z2NHey0zHQUQg+B6fn+M45Ot5Ss0S4Irnyq0y80svk1D9DIWH0E2dartG22hxZu00XaPDo6khhGZ258bm5+HkSfd7tSyQJNdm/v77Yc+d6WMMB8C56fcvC2A5DjviFwURlCH351YQRLeStbQCZsetbik+t5WVP4/aKtCVvFRFDZ/RhG4ZfGkIuNWLrtlFluTbEq9/ENw3eh+WbXFx8yJbtS03KlIQ6A/388jkI7vGF9zFRwev4opUbwbDMnYcEyXL4JLewieIhLcng8RYHH8kilCpsB5RCCOi2V0QNaSQn2i4j2a9Qlt18AhdLClCOjqMJCrU15dYj3aQQ66dgO3YhL1hwt4wlVbFHfsXJGK+GMdGj9E1uxwbPkZfoI/nLj3H3NYclm1RqBcwbANVVGnpLUZiI+iWTsto0ew0e+33RyYfYTIxiU/7yYgu+TBwl6h8ytDW26yV17h37F5evvxyz6JZ2PZ5t2yLQqPA5/d9vpfYnFRU/u/3/RIRLLYuvUsibBJLDZEIRHHMLpVWlen+vW6/s32l4rDzJu5RPB+aGdtQbIivHv0qC/kFFvOLmLbJ3tReJpOTH72HSmDQ1XV0SjsD0SoVV+excRaUg7D6PZiedvNhric0ug4vvOAK79pt2L8fVBVaLZifJ9ntsrV/knooiHdmltbBAziqiu04ZE2DpCSTvMXNtNau8drCayzkF+joHcB1hy00Cux3bAKal41mibXSGtV2lbbRpq23WSouIdT2c+LYOJrjULQMmgsLeH/wAzyOQ2B4GElV3f3PZOCHP4Sf+Rk3bfg2kZIVFASatol/l9iHum3hFyX6pPfpohudcgW0hXOgBkANgRYGbxQfDqKukSkuMxEbhPCE+/ztsfJsNctAZOCm4/wfFhRZ4bGpxzg4cJBMJYNpmwS0AEPRoVtO6t3FR4Mjw0d4deHVXf1/dFPHtm2ODB/p/S5j6HRsm8Q135Xt99Oe3kvo9TfY8KSpK1G01gZoA2AKqNEInZaXcL1FZDjOpjlMVPARrDTo+mJsetZJyDKJUKI3hTTWN8ZqcZVsLUu1Ve0Zcl5pX/3nV/4zq8VVau1aLw/ItEwE0fXQahttYv4YAS1APBDnF479AiFv6H3rrwzTwLRNNFn72K0PPmrcJSqfMlyJkj80eAjHcTi5fJLFwmJPBa5ICpOJSb5y5Cs7XjcSTPD3jv8iryshAkt/jV2tUHFMfKqXff3TV3UhxaJbHYhEPtK/I+qPcq//3o92umc3eGOQPOr6dNiGO/WzVYAzb7qjyv5RV+Q5MwNzc/DAA3DffTvJysqK+1gq5VZTtO0Lnt8PioJ/c5M9g2lWggG6pRLlapVuNEzXcUhICke9wR3eMlfQ1tssF5d54dILZKoZ9qf3E4q5JeCt2han10/jCYcYthss1xpU2jU6ZseNW5dkGu0q59ZOcUYZZF/yXiqIpN56HW+rQWd0hLCtM25J+FXVJScLC3D6tBvSd5sXrkOqj0FFY0nvMioLOyorHduiads86AkS3mVq6rYgqa4gOJCGwgXQayDKMPYzCNFJ9lXzLF56niXbYSA6iaZo6KZOtppFFmWOjxz/UKIGbgcxf6xn7HYXnxwemniI52ae48LmBaZT071KQ6vbYjY3y/70fh4cf7D3/Kptou1y/jWPHkHKFwi9/BKtzWWwFkDtQqSPzgPT1I8lCJzbojxvgF0jJLep+GUujoToixzEtExGo6OssEKmkkGWZNfGQZQ4PnKcXzrxS+xL72MkNsILMy+wVFxCU1xCUmlXEASBsC+MaZkYtkGj2yDsDVNoFJjJzrgDDO9Dk5Kv5vnW2W/x/KXnKTTdIYjPTX+Or937tY90Qu7jxF2i8inDlUjtYrPIY3sfYzI5yfzWPJV2Ba/iZSAygCqpu7qapiNpvvrw36TQDsHLL2Ml0oSTA71+KuWyWyE4dAhuYZb0mUf/fW4LKH8WCpfh3Gm3p5G+F5p9UNnWOHS78NILoFQgGXBvooEBWFp0b+ya5hIY2756o1dVcBwSpQregTQ1zYfh8SDIKv3bP97rbqS2bXNu4xynV09zMXuRixsXSYaSWI7Fvv59xPwxQt4Qff4+Zpo1+qUWntYmHUNAlFWCqg/RsRlwGujeJH+1Psvh89/lFwceYSBfRB8YRJNkyraJrbfZp/lQRdFt/6yvQ6nkktPbgCpJ/HIowR9WciybOgFRRBNEmo5Nx7HZq3r5SvgDClkl1fWziR/Y1hRJPffZwdAoX/IleWv5LTbKG5i2iSRI9If7uW/svl1HvW3bZqOywWJ+kXKzjE/zMd43zmh89CNvE93FR4+oP8pvPvmb/MHLf8Dl7OVeG0iRFO4Zuod/+Ng/JOS7OoGlCgLmLgnFjmVRK5coB/341CBUhkFug1NHz6+R2zsEX/151mfXqZRydCRoJqKkEiM8G0xweu00uXqOervuVjlbbWK+GF87/jW+fu/Xd5CCleIKOG7Ia0fvYNs26raVgSK5IbSWZdHoNkgEE2SrWdpG+44/m0w5wz//y3/O2ytv95LQ87U8FzYu8KPZH/G/fO1/+URCBD9s/ATfrT6bkESJAwMH+P7579PSW738BXB7owtbC4z2jd7Ufl2WZPo/9ywoITh3DpY33Jtrt+tWBB5+2G1lfEgoN8uutbWsEvfHPx0lR0F0qyqxfXD2NWjWIDECF1Ygf8Y1fhIEd/y3ngHrAjx1zBXYShoUS6BG3emoYBCqVfe/r0CSwDAIFEsEDh5kIHVrbcbZ9bO8OPsifs2PJmmkI2mivij5Wp623ubesXsJeUIkggnOVjO8IArstxX6qeMTAVvHtE0ygp9C6CDtaoFSZpZOcBrBMHFUFUkQiIkyJcu1DU+Jqku0DMP9uQMc8AT4xzGJH9VLnNfb6I5NVJS5zxvkqUDkOtO3DwBRvjp+fQ1G4iMMRgd7F29N1kiH07t6Tli2xesLr/PO6juYlolX9aKbOuc3zrM3tZfP7fvcbfkW3cWnG3uSe/jXX/3XnFo55ZIA3Imf3Qw3U7LGotHBdHa6ZnPyJPraCo0D+/FGE1Bvur+3LHjlLYT5ItZvjDB6/zQJvYVpmaiyiiqrZMoZqq0qa6U1MrUMpmWiyAoB1U0Tv75ycSUSoNKuoMka1U4V2dk+fgX3Wq7IittW9Lh6RMuy7ugzsW2b//jj/8jJ5ZMMR4d3kLWO3uH85nn+7ff+Lb/3q7/3mTcmvEtUPoWYTrmBduc2zqHJGgFPAN3SqTQrpCNpHp28uf16rV2j1q4hHpkiMTGOspFxhaHBoKvHSCY/lAySYqPIqZVTLBWWaOttFElhMDrI8ZHjuyb3fiKQPaD7wArDpWVXbJpMutMw3QoUNiDfhfMleHYYfB7QGyBdcttE2jiMj7uEr1qFUGib4HShVnMrFocO3XIXWt0W766+i9/jpz/Uz3JhGUmUUGWV/nA/m9VN1kprHBo8xHBsmLMbZ1nrdMgQYlQN0ye6F7V1s4sQHEHToihyHd1oU3I6jHs9SM0GViiEKAhIgkDZNkihQqMBPp/7c4eYUL1MxAdpWxZtLAJIqO9Xl/I+IInSbemZZrOznFw6STKU3OFrops6FzMX8apentr/1Ee5qx8pLMdhvVWl0qnhkRT2BJMoH+P38GmCpmg8PPkwD08+fMvnpWSFAVll3eiSlFQ3QbnToT13mfzIEMPtLqnGNZNjkkRgzz4GF09y/sJp4scfwqf63BHnaoaV0govzrxItpIlHU1zIH2AdDiNYRksFZb4xmvfIOQNcd/4fb1NpkNpAp4Aa+U1gt4guVqOpt3EY3sQBKFXKfSrfnRTJ+qL3rFIe728zmvzrxHwBnaQFACP6iEVTPHO2juc2zjHPUP33NG2P224S1Q+hVBkd2x4KDbEpcwlKq0KHsXD43sfZ7p/+sYUW6DZbfL28tvM5eaod+u9zIgjI0fYn37gQ610lJolvnf+e2RqGdKhNKlQiq7ZZaW4Qq6W44sHvsh4Yvy9N7QbsllXW5HLuZWgsTGXLPjf56pYll1dTj7vEgvLcoW1zRVwDIgnod6C9S3YO+IKPAemIfM6lDbc93ccd582N13SZ9swOQlf+AIM3vpmmqlmKLVKTCRcj4eAJ0CmlgGuxthnqhn2pvaSDqcZi42xVFhirbyG7o+xJMo4iMQDKUZjo2QNHUmSURUPZihIZ3QE34WLWIEAiCIiAha4f2cuB0ePuiZX7xNeScLLp/PGaNkWFzYvoCnaDeZrVxJu57fmOTZy7DOpNVlslvnO4ltczF6ipTeRBZnR6ABfGLuXB9M/RcnMdwhVEDnmCSIhkjXdBGWnXsHj2OxpdDjcaCNf1xqSI1GGbR8XKlU2yhukQinWSmucXjtNsVGk3CrTF+wj4o2wVlpDt3TG4mPsT+/n7NpZvnv+u5wYPdG7zk6lphiKDbGQX2CjsoEgCliGRc2oIYgCHsVDX6APSZIQBZFjI8d2NX67FVaKK5Rb5ZtW16P+KPNb81zOXb5LVH5aYds29U6djcoGuqkjiW4fvS/Q96GU2a41PzIsA0mUbioibOttN79l6zLJYJLx4DiWbZGv5/nhxR/SMTof6kz+ufVzbFY22du/txds51N97EnsYWnbPXQ4Nnzn0eBnzsBrr7lkIBBwb7aXLrli0KeegsT7mPbo73d1ObruCmOLReg0gRzIKlgKjA7CxjZRAUiNweACrK1C3XSrMIEALC25+/TQQ/DMMy6Reg9c6adf+e7S4TTLxeVehowiKrSNNqZtUm1XOTh4kKcPPM3//vL/TktvkQwle6OQgiDQrGbRVD/RQJyQL0rzeAK5VMY7t4DRF8dWZMKmDZWa+7md+Mn1Yqh36hQahZuaEoY8IbLVLKVm6TNHVFZaFf7wzF+xsrXAYCjJYHSYrmWwXFrjG5VNDMvgsc/4zWc3OI7Tu36WGiW+c+47zGZnEQSBgwMHeebgM4T87+0I7BclHvQGKdsmVcuCRofwzCJRBITdriOmSVIO8Mj4A7yhiVzMXOT02mlEQSToCeKRPQxHh5FlGc3S2KptEfFGiPljDEYHmc3Osl5e71WTJxITDEeHcXDwqT78qp9yq4zjOOiWO72kWzpTfVMcHDjIE3ufuOPwQWXblfyKG/j1sCzL1cSId7bdTyPuEpWboNKqUGgUcByHmD/WY7uWbTG/Nc8rc6/w5uKbVDtVAloAr+IlGUpyaPAQzx56Fo/64Yz6CoLwnoLAhfwCl3OXmUhO9BwOr5TOC40C76y+w0RyYtdKzJ2i2W0ytzVHMpTckb57BQORATbKG2Sr2d749G1hbc31LfF6Yeia11mWW8146SX4uZ+7cxHwwAAk+uDUy+AXIeAHnxeaCpTbIBtgdcC8RschijAyDOOHYKXtVmPAnRA6cAAmJt6zfWZaJi29hYA7OdM1u2iyRswfYyo5xWx2lma3uZ2LpLJR3nBDA6ce5/DQYeqdOn9+6s/Bcb/LSqtCW2/T74tQkjVCsVGiwQSmIFD54hfwXbyIfXkOf6tJJBhxtUiHDn3k013XwjAN1svrNPUmiqQwEBn4SC3Ahe3/XclF7NoWZcukYBp0HBuPIFCzDPRdhJWfZjiOw4sb51nZWuBAaqpH+BVZZTqxh/nSGt+bf5XjiUn8PwFeGzXLZN3osmnqWDj0STIbK6f4g+//W1aKK9jY4MBfvPsX/PFbf8y//Mq/5NjoewcR7khQHhyB4RHXcmA3orK5iZBKMfn4lxkO+nlh5gWq7SrTyWk2q5tczl3upTVfIQjFZpGYP4Ymaxi2Qde8GuDqVb30BfuYSEywWdmk2CwS8oZ6k5sCrqnd/vR+nj7wNAcGDtzx53Zw4CADkQHWK+u7Tgvlm3n6An07Rrc/q7hLVK5Dx+jw1uJbzGRnqHVqCAj4NB+TiUkeGH+A0+uneenyS5xZO0NX76I7OivFFSRRIuaPcXr1NJezl/mNJ37jlvbzXaPbu6B/0Iv5THYGv+bfYcN8BXF/vMf2Pwyi0jW7dM0uIc/uqxpN1jCsnSftbeHyZVf7MXKdvkWS3PbL2pr7M36HLaVuBVJ1UEqu2Z1TA0MAuhAPQmoEtoqgXEM8jKY7hbL3GBxLuloPQXDbT+/RQtNNnXdW3uGluZfI1XKIiLSNNuVmmROjJxAEgankFAEtwFJhiYWtBfb172O6f5r96f2M940jCAK/ct+v4FW8PHfpObbqW/hVP33BPsLeMEf69xIcvZ91SycoSBAKsHL/vWj3HOIwEoFQ1CV8t4t63a0Wra25ba2BAdcoLnrz4/d6rBRXeG3+NbK1rOuc7EDEF+HI8BGOjxz/SETWQU+QZCjJZmUTQ9GY19usGV3ato2FTbtVRUXgDBJRo0v6M+KBUjENzm1eJOEN71qVHAqlWSwscqm4zL3v4wb3acKWqXOqXadqW/i3W5evZS7zh9/61zRqWQ6m9vYWfe1um9ncLP/iW/+C//x3//N7xihYlsVrC6/1kssf+dzj+GZmXGuC8XFXbG5ZrudQpQJ/429ALIYGRH1R+kP9JMNJ2mYbVVZpdVu90WhVVmnr7pROpV0hqAV3+Ps0u01KjRKf3/d5ukaXufwcmWoG3dAxLMN1O1ZUvnTwSzw88fD7Oj+C3iBfPvJlfu9Hv8dGaYN0JI0oij2vrXq7zjMHn2Gsb+yOt/1pw12icg0s2+Klyy9xeu00/aF++sP9CIJArV3j9NpplgpLFBoFLucus15ZxzANis0ioiAiCRKWZTESH+GNxTfoC/bxN+77GwQ8gR3v0eq2OLdxjpnsDK1uC1mSGY2PcnjwcG+6505g2zaNbuOmKceC4Lpq3jFxuAmuRN23jfau79k1uyiScmfmcbbtjtHebPWvqu6kTrl8Z0TF7MDqcyAVITkIihe8sps3I5RAMdyWkCqDtn2hsE3XkCy2D3wpl6CEbi98Tjd1/vitP+a7577rXow0H6ZlutlJmwaNToMjw0dQZbVHbO+9714+t+9zxAPxHS1DWZL5heO/wIMTDzK/NU+5VcYje3pjtw1BZM3skjN0HGBC9TDsD9/SaG5XbG7C88+72qArotuZGbcN98QTbvXoPZCpZPjhxR/S0luMxkdRJIWOZbJZ3+KHsz9CFMSbBiB+EIiiyKGBQ8wWlpkvb9BUA1iOTUySsYwOxU6N+OA9rEsyJ9s1HhUj7ur6A8K2bS5uXmS5uIwgCOxJ7GE6Nf2hkbGW2aXVbRFVd6+WaJKEjUNdv/Nx1k8TdMfmTKdBy7F3pJAvzDxHsbLOUP80XNMO8WpeplPTzG3N8Vdn/opfe/TXbrrtF2de5Pdf+n0u5y7TMTooosJofJTfePQenj1TQpiZcbVnALEYfO1r8PWv916vKVovqmQ4OsxgZJCl4hKjyiii6AarelUvXbNLrpbjF4/94o6FoGVbWI5bPYn6o/RH+tFNHd3UkSUZj+Lhcu4yiWDiAx03v/7or1OsF/nW2W9xfuO867flWPhVP88ceoZ/+vQ/fd/b/jThLlG5BpuVTS5lLjEaH8W3fZGwHQdb9SKE03zz/PfpduvU6nkE2ybfyNPsNHulyWq7CrgH+amVU9w7ei9HR472tt/qtvjhxR8ym5t1zaQCsd4o5Wpxlfv33N+zY06GkrflASGKIkEtSLaW3fXxKyebR/5wWlF+zc9Ucoq3lt4i4ovc0P7ZrGySDqfpD/ffZAs3wRW/kuth1cCsAFno5sC24HYNv6rLbkihJwn7OrBZhY4OXg84/VBYBk8JJuIg1d3nmi0IjcPgo3c8HfX28tt85+x38Hv8pMPp3mcz1jfG6ZXTLBWX2NO3B0lyk7Cf3u+WfG9FMgciAwxEbgzA04C4rGBrDg5ujMEdo9WCF190dTt7916tFjmOq+d58UW3qhK7tb7jwuYFqu0qe1N7aVgW59s1VowuXUGmbdmsX36ZQHyMvYEPXycylZpicux+Li28SrmawyOrdGwLUZIYGTjIxOgJmkDONFg3uh+YqGyUN/jDV/6QCxsXaOktHBwCWoAjQ0f49cd+nUTog7vmBhQNj+qhqbcJE7nh8Y5tIQHBD8lJ+pNC1tQpWSYD15AUgAsrb+NRvAiCRNt28FxzidEUDUEQeHv57ZsSlVfnXuVffutfUmgWGIoMEfKGaHVbrJZW+ZfSJrVf+g3+pvh111/I63UF5wM7z7HByCA+1Ue1XSXsDfPE9BPUztRYzC+iyRr1bh2v4mW1uMrxkeN8+Z4v73i9T/UR8oSotqu9xeqVUWfAtXSQ1JtWpm8XkiTxOz/7OzxzzzM8d/E5cvUcUW+UJ6ef5P7x++9Y9/JpxV2icg3WSmuYttkjKZbjsGp0yJg65jYxqW+nqNbrWzRbVRRJxiN5sGyLttGmZbSwHItcNcfZ9bMcGT7SOwkvZS5xOXeZyeRkr03jU30oksJLl1/izaU3mUpNoUoqfcE+jgwd4eDAwfdk3Pv697FUWNrVYrpQL/Rixj8sHB46zHp5nbmtOdKhNH7NT9fskqlm8Cpe7hu7787cQ0XRbe+89ZbrBguuq2xnBhrL7vRKqQaX2qAVYd8XwXcbjov1tW0TtzCEvTCQgmwJ8lX3Zjw2CHE/VGtQWoNOGkYecz1YlDvr/du2zYuzL6KbOlPhqZ3VEVHm8NBhZnOz7Enu4eGJh/Gq3g9sRmbbNoIgIL5f8fbyspsTNDR0dYQdXII2NORWVhYWbklUmt0my8VlEoEEDcvi1XaFrKnjFUQ8gojgi3GpuMh/2rjAPxy/lz03IWXvFy29hRBMsm/PI2y0Snj0NqrqJRYZIBLqRxQlGqaBKAhsGjoHNef9kTrc0f//8MJ/4Nz6OfYk9vRW0KVGiZfnX6ZjdvjtZ377psTzdhGWVY6l9/PdS8+TDCZ2tH8cx2GjtsWAv4998duPRvjE4DhgroE+D2YWBBmUCVAnadkKAjeSbMPUEUURSRTocuPiRRKkm1aIHcfhj177I7bqWxxKH0KU3Gun3xtiMpJmqbjO/3f+BZ751V8hcgtRbiKY4NDgIU4unUQ3dQYiAzx7+Fm+efqbzGRmsGwLj+oh6otSaBT4y9N/ybOHn2V0+zuRJZmDgwf5wYUfUG/X8aie3jXfsi3Wy+vsTe699UiyVQdj1V2sCQ5IcVBGQNx5fAmCwLGRY7umS/+k4C5RuQZto71D51GwdDbMLgFBQlNkRMfGMbs4OLSNLo7gjpkJgoAkSeiWTrlZZiw+hmEabFY3sWwLWZIxTINL2UuEfeEd79ExOpxeO029W3eFuz630pKv53nu0nMYlvGeZfOJ5AT7+vdxMXORRDBB2BvuTf3ols6Te5+8YXzzgyDmj/Ezh36m56OSr+dRZIWx+Nj791GZnnZvjOvr7uqmcwGWT8NCHTbLEAmDUIOFb8GRRfjKP3XNwrpd1xdlt6Rg23BdT1MpV4OhSTA9AnsdaBUgcwbmCpBRoR2B/Luwvw1PJiA1dUe7r1s6y8VlIv7IrlNfmqLhOA7lVvkDRbc7jsNaaY3Z7Cyb1U1EQWRPYg97U3t39Mi3alvMbc31zLFG46NMJievXhg3N+Fb34KzZ2F11W2vpVKuNuVKCy4QcL+P++7jZrBsC9My8Wt+LnWbbtaRqCJtZ1N5RYmuqFAxdJ5rlPl7EQ35Q2iRNLtNTq+dZjYzy6l6nqpjIwUTDA8eJhG77vgTQHAcHBxsHKT3mW/92sJrXNi8wIGBAztam7FADE3ROLN2hpPLJ3l87+Mf5E8D4PNDR5jdWuDi1jwDoRRhb5iOqbNZ30KxTX5m+okb2sqfOjgOdN6G9huAAULQTcQ2VkC/hCw/jM2Nf8Noai9L2dldc0Vty6Zrdm+a7H45d5mzG2fpD/X3SIrpjWCG01ian3h8gmKzxB+unORv732M/pssFgRBYCw+xusLr/P989/vTeZJosSR4SN4FA+JYAK/6qfUKvHi7IvUO3W+duJrPSfYkMcVz37rzLeQRIlUMEU8EEeRFYajwzw8eRNtirEJnZPQ+AFYeRAUEPwghkAZgMDPgfbeLdmfJNwlKtcg4o2gm24urOU4ZA0DBRFt+2C6IhT1qj5EAQzH6Z1MtmMjiiKGZSCKIqLkipquVBY6Zodmt3mDcHazskmummM4OkyulkO39N7ERL6e552Vd5hMTt6SaHgUD5/f93mi/iizmVnWSmtIokQymOSeoXtuelJ/EMQDcb548IsfnjNtKgWf/7w73XP5FNTfhosFcGSYGnVvoIoKlSq89AaUZAiNue0LTXNbF/v379ST+JJunkx8yNW2zM25pV6PCKuX4MImVP2wd8oV8bY68No7sFWB/9v/AyK3X8aXRRlJkOhYnV0fd7ZvlCLv/ybtOA7vrr7LawuvYVgGYW8YwzF4feF1LmUu8dS+pxhPjDOXm+PF2Rd7ZWuA1xde592VdxntG4ViEf3UW+yZzzKpSAQSCQRddzOOKhU4fvwqWXmP6oNX8RL0BMm3KqxLGj5B6JEUAN3oIMkKg74QG2aXZaPD5AecVGl1W/zg4g+Yy82RCCbYEx1ktl0jV97gXD3PganH6N/2rbEdBwGwBQhLMvL7JCkAp5ZP3VR/5df8OI7DmbUzHwpRGfRH+I1jP8dfz73Kua3LbNXzqJLEnlCaL4zfx32DtzYa/FTAWHFJihgG6aowu9lpsbL5DkvNGebEYxixUYZjI6iyK3Z+6MAXOTn7ItnyGlPxsd7rHMdhsbhIX6CPZw8/u+tbVpoVumb36pSmN4LeNw4ISO0aHsdCb+QpmDon2zUe8IZ21XW9fPll/q83/i+y1SyiKNLoNMhWswS9QaL+KIqksFZaw3Gcnp5ltbjK28tvMxIbYSG/wI9mXH3WkaEjZGoZMrUMhWaBp/c/zbOHn919waIvQuO70HoRrCaIGlg5sDsgRsC4DPoMRP4+eO7/UMw7Pwu4S1SuwWh8lKAnSKFRwOuL0nZsfNs6g2anTl+4n2Ith+PY+LQAHb1N22ijSgqGbYADPs3Xs+wejg73VteKpKBICl2zSxCXrNi2zVppDZ/mQxAEHMfZ0TKJB+LM5eZYL69zwHtrdb9P8/HI5CMcHT5KveMavsX8sTv3MrlDRP3RW0433RYsy125nzvnijpX591/qxE4fgDiMXf6B8Dvg3wLvvktePZvuxqKTgd+/GNYXIQvfelqqyI8Dp53oZ37/7P330Fy5fl1J/q5Pr2trCxvUYVCwQONQaN9z7SZGZLNGQ5nJFGGTqREcRm7EhWiJG7oLakNcblBSquluBL5gi6eqCV3SQ5nqHGc6Wlv0A6mYapQKO+yKr3P698ft5BANUwD3ehhd0+diIxAITNv3sy8ee/5fb/ne45HYoJB72K8fhmmFsHywYl7oHcrtC8UgHgYpt+G156GJ/72bb8FWZI50H+Ar539GgPuwHVVlXKzjE/2MXaHlZprsVZa45W5V9ppq1eQjqRZyi/x3MxzKLLXRrQdextBtRyLpy8+zfLJZeI1A6WpEw9pPFyyOFpW2BsfROnp8Sot8/Ne375W87xYboErfj9/cf5b1H1RotdcxF3XoVzOkIz30RXtYc7SKd/E8+FOMJWZYmZjpt1CVW2LAgKW4qNYXmdu6S2SsV5kWaPkWKiCQFiQGdyqfr5XXNEV3AyKrNAwGje9/07RH0ryjw8/xVo1R6FVwS8pDES6Pjq6A+MSYG0jKcvFLM9ePsdGNY9hZpnRV3jRkOmPdfPwvs8w3L2HvcOf4P6jX+S51/+E+fWLxP1RHNeh0qoQ9Uf5Rw/9I/b27r3hS3aGO/Erfmp6jaAWxoykQRCRtvSDLVNHAfrVAC3HYcZokpKUbcfFpcwlfv/F36du1NnTswdFUsiUM7SsFsuFZTLlDKOdo4T9YUREdFOn0qqA602/TWemeXXuVQRBYKJ7Atux6U/047gOhm1Q1ateIOE7iYrT8khK83kwl73/Mzc87Z6ggVAHdcyruJT/zCMu2t1fhH4YsUNUrkFHuINjQ8d4ceZFso0yTdUPiLRaZUzL4BMTn8KyLeYzFwn5wtRaFVzbxLC8KkjYHybij2BaJj2xHg71H2pv26f42NW5i5NzJ0kGvQkPx3UwbRNFUig3y0T8kW0XIFEQEQShXeW5HQS14F3LNslWs8xn5ynUC/hVP/2Jfvrj/Xf3RKnrXhXlm9/0LpCuC/U8LOUhKcJS1mtBBLb6siub0DRAk73qSDTq3To6PL3FyZPw6U97Kw1/EnofhOVnoTILnVFIjsGbl2AuCocOQ8c7LsSKAtEQnDsDD30OfLcvWHxk/BFem3uNS5uXGE4Oo8oqrutSbpZZyi9xdOjoTU+wt4PLm5dpmS0G3tnaAPoSfVzauMSrs69SaBTYnb56AsuUM5ycO0m+lsfVDbpNjd7kEFVX53RlHidzBlEUOBDfavtkMp7RXjp9W1M/kz2TTBWWeXPuJJYvRNwXxrJM6o0i0XAno0PHcAARAfV9rgBtx2YqM0XUf7WFGpFkhlUfuuvQDCZZKi4Tyi8SSw6iIpJWFSZ9AbrfpyaoN97L2dWzN7zPdV1aZuvOReS3gZ5wBz3vMor7oYPrgLUGwtWLcblZ57uXz1JuNvCrPpbyFbAlZDfKqfmTzGzOcO+BH2L/+IP87Yd+lif7D/L8ua9zcf0iAgIP736Ypw4+xQNjD9z0ZYdSQxwZPMLTF54mkRjE0UKIes3bJSBXzdIV7WKyZy+2KJKzTSqOTfSaBd3zM8+zWd3k8MDhNoERBAFRFHFwMEwDQRDaAwqqrOIKLoVGgY3KBnO5OQr1AmPpMZbySyzmFz0igzdS7+JyaePS9RER+jlovgRW1fv8nCzYRWh7BWlgAnKHV63ST4M66ul+Pub4+L/DO8ThgcOEfWHeXj1HJjdP3rHpC3cy3D3BQGoM13UoVDewXRdXFNFsC2erTx/xR1BllYH4AA+OPXjd6nlvz14Wcgtc3rxMb7wXv+JHkRTWymsE1SDjveNo8lWvB9uxweXORn3vAlzX5fTyaU7OnaSm1/ApPkzL5M3FNxnvHOehiUeIvJ/+uF3whHWuDa/NwNOvepMnAwOeoLOwCsU1byR5YRUUGQ7sAtOGTM4bMXZ826eEJMnTtiwseNu6khacnAAtCsVL3lSPpEF0DEL29STlCvyaNx3UaNwRUdnTs4efvP8n+W+v/TemMlMokoLruu3x3J+6/6fe13eZqWRu6rkjCiKyKLNUWMKv+NsnWMd1WMgvkK/nvZFoX4RmtYygKkRQqfT0skiOsZXL1OoiIUHxpiH27PFacbdhGOdTfHxh36dZ0MK8tnoO09SRRJldQ8fo6txFKBBn3dKJbxGK9wPDMmgYjevIeFpWCYgiPbLKm+V1wo7NqOpnWPGxS/WTltX3Ljrewr0j9/Lc9HNslDdIR7eLIFeLq0T9UU6Mnnhfr/GxgiB5VgBbmM1l2KyWSQSCPHv5PLVmEUXzE9MidHYMkWtWKCy+gRLu4MG9j6Pu/zQ/uv/TWLbl6QBvQ6AvCAI//cBPM5OZ4dLmHKnkMH6ziWEaFOoFglqAxyefQJUVLNfFcuxtScuWbXF2+SzxYHxblUWTNS800AEEqDardISukkfbtvEpPkrNEi2jhSzJXNq4xHRmGlVW2637WqvGZnWTF2de5OHxh7e3yvXznnAW0dOmuGVPn4IGWODo4DbBtoASGLNgbXq6lY85dojKOyAIAmPpMUZTo4xVc7zVrNIZiBDdWo2dmHycjXKGt+ZfY0D2YTZLAIS0ELZjkwgleGD8AR6ffPw69X8ylOTJfU/yyuwr7bwIURRxHIc93XuuM+bZqGwQD8RJBBM0jRv7lnwQmM/N8+LMi4R8oTbrNx2HdaPGXy29wZtWi+MTn2SX4mdA1VBv4FB7I5TqmyytfY1a7SKaaNAn++l8axahUgc5dnXqJJTw/l2qgh2AbAGKFajWYGUJ3BaQ9KaBYrGr5mbhsGfeVKtdJSoAoW7vduWE1EzASzMeEXqn061rgWlBNH5jge674KHdDzGWHuP1hdeZz86jKiqH+g+xt2fv+xY0y6J8U7ts2Epk3YqQv4K6XidXy2HZFj7FR8swqGExU1nBEkGVFDa6A0QSYQbVGCEn6E38fO5z79r2uRaaovGl0XtxkkPUjRa9mh+/4sN2HDYsHd1xeSgYIiS+v1OOIiltH58r+psrCIsyAUXA8Yd5KtnPWDR9V1NjD/Uf4jP7P8NXznyFfD1PKpzCxWWzsokkSnzpni8xnh6/a6/3kYYggjICzZe3FhUllnLn8Es6Ly8sczm3SkgVkLGw9SyQRRAEOpLDFHPzVOvFdnX5TtvXhwcO82tf+DX+88t/xAWjSa1ZQXZsRlLDPLr70fZwQst1UAXBCy3cgu3YnpbsHee0iC/itehFAcuwcLlKbppmE0EQCCjeBGcqkuL82nkylQxRf3QbqdZCGg2jwXJxmfXy+tWqiuuAtbJF7kqwZXnhtXwEcBXAAKcBggFEPJLCVSL4ccYOUbkJRFFkbySFpAW5ZHqOl4ogYLouR498nqGOAYz8EsVajmqrim7qdMe6eXLvk+zr3XfTi1I6kuapg095aZqGZ6F+fvU8l7OXydVyRPwRLNsiU86Qq+VIhVJ85fRXEASB/ng/kz2Td3XU+J1wXZcLaxdwXKe9Ymg4FmdbdRaMFiU1zOzqeYqJQZKhDvYp8IRWImgtAhbIaVB3gdy9bZtnV87y2tT/j1L1IpIcxXY10sUs988VGdIbSJoFTrc3eqf6oXcMKqehVgJNgEtTnstsswxWAGIhb0roivgzEgHT9CorN0uXvXLRGj8GXV+BlVkYHPNOquCRlPoGNBU4fuQqcbpDdMe6eerQU+/pubfCaGqUueycJ9x+x4lUN3UkUWJfzz7eWnqrLeR2XAfb8TI/bMcmq5cJSBZmrYAcCFJ2G1StJnJI47GREYazjme9fwck5Qp2aQE+H03z3XqRdcvENZogQFyUOREI8cD7mHa6AlmS2Z3ezbOXniUVSl23ys5WsySDSXrjvXc92l4URf7BiX9Af6KfZ6ee9SaqBDjYf5BPTnySB8cevKuv95GHPADWl73JH/w4xhKr+RzTmRxR1U801AtSCgQBy7ZYLiyTr+dJhpNsVjdRRIWT8ydZKiwhizKjnaPcM3jPbcWTHBk8wu/0H+Iv1i5yqVmmXw0wnBxsVzAc16VgW4ypfoLXHEOKpDDUMcSLl17c9jsTRZGeaA/LxWUaRoO6Xidfy7fjG0K+EAE1wO70boaSQxQb3pDBtZN43ut624z6oszl5q5p/1ggblWpXQsEFW/jDiB5/xYARBzBxBAC1K06y7qDatfpUlTionzXj/kPC3aIyi0gCgJ7tABdikrGNKg7Nj5RJB2MkUgOUqjlyNVygFct6Qx33taBIoriNhfagcQAZ5bPcHH9IuuldURBpK7XcXGRJZmAGsB2bC6sXWAuO8ejE48y0T3xgbznhtFgvbzeXs20HJtXG1UuGQ2aroOt+KhUMixWNgmpNvXmK1yWi+zzdyIJCpgLoL8NvhPgOwSCwGx2lucufo2gm2F3ehxB8n6QdnOdlXKBgKnRLTfAzoO4lfPTNwqVOizNQ64Ibh1SaW/kuGFC1PVaFJmMFzr4xBOwuenleKTTN35zVxDphE/+bfjyH8DiNCQieGNcFlQlGDwCB+/Qk8B1vZ68uQBOCYQQKIOg9N21HvJIaoSeWA9z2bm2Ayx4oZQL+QUmuyc5NnSMXC3HbHaW4Y5h/IqfoOpNpMzn55EFma70EGo2Dy0LXZOxRZV8o8DmwkUYvN8T0r5H7PUFGVX9zBhNao6FKoiMKX4i76xc2WVvmsF1vLFLuesqYXwXTHRPMJ+f5/LmZbqj3YR9YSzH8qbmLINPTnzyrum03glRFPnUnk/x6O5HydfyXp5MMPGBRAR8pOG6YE57x74QBwy6QgHmixVkdDTZ74lBt86XoiiiKRo1o4Zu6lxYvcD/9d3/i8XC4tbmvEGDie4Jfu7hn/Om194FoijyePduos0KBdui5jpojldJKTsWKUll/B1ValEUeWjsId5cfJOV4gq9sd42GU6Gkiii4gUOJgao6jUUUSYRjNMb60UURC/1Pt5HIphgsbBIy2y1272GZZCtZUmFU3RHuyk3yte8sgJyvzeG7K6BGPZ+I24TXBWwwHWwRR8NQlSsKjlljDkniKnXmTaa7FJ9TGrB9+wT9GHGDlF5F2wLtnoHOiOddEZuw3jsXeBX/dw7ei8H+w9SbVVZKa7w3KXnGE4Nb9MkxINx1kprvHz5ZXpiPddVbRzH4eT8SV6+/DLz+Xn8ip/jI8d5YNcD70nk57ouM3qTRbNF07FxBQG/KGEKIgo2HearBNjkvDNMSkjQc0VfY+eg9TLISRypj3Or58Cp0BlUQbqqbdFSCaRkjM3lIsmahhopgNzjXbBkGbr6IBCBtRUIdHhTOmERhJp3IlRVr4py6pT3+JERePDB20o15tijoITh5e/Axgq4AgRisG8P3PcgdN9BnIFre2OY+imvjyz4AMP7W90DgYdBfP86o4g/wmOTj/Hc9HNeWNtWpo4iK0x2T/LI7kcI+UJ8as+neHbaW/Hbjo3jOtRaNSzboi/ZhxqKgqTgFApUqzkUBHq0BIXOKPbjjyG9G9F7F/hEkf2+mxAF14Tma6BfAMcTGCIoHqnz3+8JBW/jc3hi8glem3+N+dw8mUqm7VNxaOAQE10fDIm/FqIo3hUX2o8t7A0wpkE7CJoCdpaO6DyitIog+cg1mgTdTSQ5jl/xUzfqJIIJXMclV8vxlTNfodqqsqd7T9sYsWW0OLd6jt9+9rf5X37of2nn7twKUUnmuD/CvNlixdSpuDYKAnu1IMOqn9ANdC+H+g/x1MGn+Kszf8VUZgq/4seyLZpWk13de0mO3Iu/Zy8pUUFwbRLNEv7SKuOROKNRC9k8w4m+NKuFCNVW1au8CJ5RXVe0i309+8iUM21jUcAjbNoej7DbOUAHJ+BNAqGDI4KUpCUmaLgWghRBCH6y7QNTd2wuGg0CosTo90gi8L3EDlH5EMGv+vGrfs4snwEXgmqQ+ew8q6VVbNcmEUgwmhplqbjEUmGJfdd4KViWxR++/Id84/w3PGGvL0LOznFh7QLPX3qeX/jkL9zWaGxADZCOpFnILyBqQTZtk4ZjY7sQEkUso4Gi+hkK2KTZYI0uNMdl3dKvEhWpA8zLYFyiIkTIlDOkQlFwN7e9lhXUcHf10lzO0bIs1GIN0o5HVJpN75ZMenqTkZGtSZQuL59nZcUTuwqC97j5eS9defI2Q9oEAQ4fg4l9nq7FMLysm56eGyc023mwS4AAcicOgatKfmEJqXUSxDSo15BHpwn6GRADELg7bYF0JM3nD3+e5eIypUYJQRDoDHd6lv1bq/pkKMlTB59iubhMvpanZbZIR9J89fRXqbVqmJaJLMk0Ay6qL8l4coT9vQcoh8JUY8EbmLbfJbguNF6G1msgdYIytmVC1PCcS50GhH4ApHfX8sSDcZ7c9ySFeoFqq4osynSGOz86o7sfd1gZXLuJIIc83ze5H18sSihwlmp+lo1aBV+9hSgXkQSJdCTNWOcYi4VFGkaDfC3Pwf6D21p7PtXHnu49TG9M89rCazyy+5Hb2pWIJHNQCrFbDWC6DqogosFNA0YVWeELR77AWHqMFy69wHp5HVmSGe3ZT7X3IBeNBrVWGcdo4ogi5UCCnmCAg5EVwtazYAnsjhSYTDTx+7sxhB5cQSCkhUgEvdgUQRAY7nhHbpm6yztPWMu0VbtWCS9C3cWWYrTsFqbUQ9P/MA316vk/KEo0HYc5o8mAoqHcZnXyo4IdovIhRLaWxRVc/uzNP2M+N49u6e1wwc5wJ+Od41SalW3PeW7mOb729tdIR9Lb+qKWY3Fh9QK/9+Lv8as//KvvatsuCAKTPZPM5+ZZq+ZpCRIWIAsCjm1Rq2yQTO8m4RdQLBdLVBBwaDgODi7iFUMtIQrmCo580OvLSn5P9+XanmBsC5W9vTQ31tm9Voe8AfmtsDBBgN5er0KSy3ktHsvyRpXB06RUKh7BiES8iaFg8M4NkPx+jwTdDHYVmq+AOQdOHcd12ai3OJ8TeD1jU2rWCAuLHEgneHS8l/C1ixnR712Q9WnQDoH0/lKyr0CRlbb75bs9ZiQ1QraaZS47RywQo6E3qOgV/IqfgcQA+3r3tbd1xX/nA4OdA+MCSN0gXaNXEQOerbo54xEW/+0HGCaCCRLBu58htIP3jpxlsqrbbFjdVJwQ9tbPeSO7wYLu0BFPkwpqrFcruIKGIqs0jAaz2VkGkgNIgkTYF77hlI9P8YEDF9Yu3DZRaT+3PI9v5VnYPOWFj8bHPfuC9NHrSIsiKxwdPMqh/kO0zBaSKPFcq84zjTL7/WGkSAc1vYbrgESLrFDidbGD3YKKIGn0JYeZ7DI4vXKOzriPeNw7pov1IpvVTQ72HbxeaygoEP5BcA2of92TqMgKnrWyhuUaVMRu9MBjVPyfxBG3V5SikkTOtqg6Nglph6js4AOGLMo8c/EZ1sprXpbOVhndsAwy5QyblU0enXi0/XjHcXhu+jkEhOvEW7IoM5Ia4VLmEqeWTnF85Pi7vv5oapT7Ru/jm7Mvk60VaLkuLdvEB8SSw3QPHAFhEbjqtqoJ4lWSAnjiL5GwFiEeiFNq5AloCe9iJV9tLZRcg9on+rwpm/VRWNnyjOnvh8OHYXDQS/c9dWp7S0eWPWO3atUbIe7t9TQrdxNOC+rf8apDUg+u2sPlzDRvLpxkpbDA+ZzMUs2HZOd44XKAk6t5furYY4ymrmkbiXEwZ72KzF0iKneCtdIa3z7/bZaLy/TEetAUr+rVNJoMJAbapfX53DwDiQFC2vVj57brIsL7F+pZa+DUQe27+n+u4bWDBMXTqpjT4Dt8R4TTtU1ymxcp1LIIsp9Uajfxa/yIdvC9w4LR4qxeo2VrtBwfS45C05UIuTqZjQXisS42yzm6wwFGO0coGmEaRoNys4wkSnzh8Bf41oVvITVvPoosiAKmZd7Zjq29Amf/v9DYBF8MkDx/pbVXYdfnYeJv3bDCIokSQS1IzbE4pzeIiCI+UQIk4oGERyb0swjGJvOOj5XGOfqlBpLcycMjEwQVmansEpc3fIBKNOCNsB8bOnbjaSZBgcgXQZv0RMjW5laL1EUXurkk7Cfom0QUteuf+zHGDlH5EMLFZaGwwGBicJsoUJVVOiOdXN68zGx2tv3/tVaN5cLytrn+axHyhTAdk5XiCsd5d6IiCAL3DN1DKJrGWDyNU8pQFUVi8T5SsT5kRaNqJ2i5Mqqjo0lB0u8suTtF0D6Bomjs7d3Lt89/m4ZvmIA4DeYqiCFMB9YKSxzsSRPf/xTc/+C2aksbe/fCN77hJfomk1dPKPU6lMue54coeq2buwlzziMZyigICoV6ga9deIVXF+dZLmZxcSkafnBaBDWbF+cuEFJ8/PS9j9MZjt3dfXkPsB2bV2ZfodgocqDvALIkM52ZpiPUQTwQZ7W0Siqc8nRQLtsCME3XYc0yWDRalG2LuuugCN4IcJes0i1rdEh3OmVgbZ3Yi9BcB3MdxAqoGoiqJyRU+9may8R0HWq2BbV1wuV5ZL0Ash9iI158gqxRzZzh5VN/ytzmDA2zBZJCKNzN7l2f5N69P/A99yD6fkbJtnhbryECnUonb+sZgk6VbjnIcrnESq3C3q5BYqrEpc1FcmYDnJY3Oi/76Ix0sqdnD/P5eaYz0zd8jSvZUu+0crglmgU4/0eglyF18BrR9gDU1mH2y5AYg657brqJomVRc2yS7yQXTgHMBSKCQ5Y4ebGXfmENzBU0scT9Q4c4kA6TFx4ApZ9kKHlTL6Q2BBF8+0Gb8BZ2W4JzRQjg1EtUXG7Ynq06NiFBuqHu5qOOHaLyIURdr6OKKi2zhSqrbeMw3dKpt+p0Rbq4tHGp/XhRFNtOt1fQMlttO+8rgXjyHXhYCILARLyfz/iiyNUCBduk5NpUXRfRtii6ceJCH0PuHDF57Gq41xVHSjEKqueOuqdrD7lqjrMrZ8HtJChqGHqGptliLD3BiT1fhMDBG5MU8Hw9PvMo/Ml/gdxpz7TNCIIS8jJ+BgY8ncngXU6T1WdA8G+ZLsGrC+c4uTTLeqWMKPpIB11iTox8vUbdaNA0TV5ZnOL+kYmrRMUpepUC6Xu/wl8rrbFcXCYdTpOr5oj4IqTDafL1PLZrU9frvDr3Kg+OPciD4w+yq3MXAIbrcLpVY95o4bouOWOThrWB4DSICxZNNcqiMsIuXycTW/EPtwVbgOIU1M+CvuhVVyQ/+BKeu7B7GowObLGLOWmCBSdBtbwIlSWidoshwWGotoKUvwDxXeiBHp5+9feYKWXoTQzS54vgWi3K5VVeP/P/oNsmjx3+4p0lee/gPWPN1Kk7DgOKxqZlUhe76XAXwc4RsOu0bANDMEkFFWblKNVmjZgkEfAFkEWZarPK3OYc9++6n+cvPc9SfmlbwKnrusxsztAT67kzY721V6G6Aqn910+Whbohm4XVl25JVBRRRBTA2ppHNm2DptHC7y6hoGORQBAEZEHwWr6CD+x1TH2GWlNnuvI2m60F4oE44+lxhjuG311PJSjbbB78wIDi4229jl/0qthX0HIcao7DYZ//tn2tPkrYISofQkii1E5BLrfKWI63EtVkjZ54D7hgWiaO4wUhhnwh9vbs9bwlwinWymvkqrl2FLppmSBAd3T7JIvtumRtk7Lt/fxCokSnrLQPdEEQOOwLsWEanGxWqFoOTcfBxEZEYEo6Sq+gcFjOo5lVvHl/0xPTBh5st3gUWeHh3Q8zmBxkZnOGQr1AZ1JjLDXCSOcEvlup1F0HNk5B5xo8mPKyenwW+FSI9YHc56X/vkfvj1vCrW/5GYBl27y9tkBD12nZFulQDEVqITqgqVEcV6dpuuQbVU6tzPPA8F5EdLA3wX/ihm2fWqvWNi57N+3Qe0G1VWUlv8JSfolKq9I2hAtpIWKBGJZtYbs2n93/2W0r1HmjyZzRolOSWatPETEu0i06OIKfrKshGjMM2JeYdR8kIo3Rq9xGGdqxYH0OqhWQy+BXQBzxvt/6KhjzEJNxUHm7eo5pySRgCCRKVfClqPiTvAk03DT77DpC4SILU99gtrTJaHoPytZKV5B9xJIjqKVFpmafZ3L4/g/Ud2gHV1F0LPxbpNVwHUQpDNI4lcYSeT1P07K4sFmkUdgg32zh90eRbYNoIIoma+iWzumV03zpni/xpXu+xJ+8/iecXjpNLBDDdV1KzRKpUIofv+/H72zasr7mEZSb2QRoEc+5+hboFGW6ZZXpRolWfpHV0gqC02IgWKUzKGH4ISbrDAlXJtkEdDfIevYk81UfK2YKRI253ByXNi6xt2cvj048ese/+zHNT921WTR0BEAVBHTXBVzGVD+j6l2uKn9IsENUPoToinShSipDySF0W0c3PcIR1IL4VT9nl8+yp3vPNu+GRyce5c3FN70wLATCvjDBYJCm2SRXy9ET7WGpsMSB/gP4FB81x+ZMq8q6aWALtP2EOiSFQ/5QexzbL0o8FoyTt0101yEkiUgI+AWBiBiiKT9BTqmSlMqe1kBKgDJ03YVZEiVGO0cZ7byNeHLb9iokzSY05qD1NgSTcPyHIDbtBefpWSi8DgEb7nkA7r33xtM67wdS0iMaQKFRpWro6I6FLIiIgoAA2IggBNCUGFV9E9uqk6+u4BgziJLsjWf6jm3b7MzGDN++8G3eWnwL3dKJB+M8NP4QT+x5gkjg/bnXbnudzRkuZy/THeumM9yJJEo0jSaFRgFJlBhJjeCTfaQjVzVDpuuwZOrYeo2p8jQt6wyuI1FSEoR9CkElyKobYtA9T4f+CstqFz2y+u5VldoqlFcgcASsb3gHm9ACVwetDEYDrH1kAweZdTroEDX85VPgaCD30YFNHYHLgkqPZJGUA8yun0PxDbRJylUIBIIprPwySxtTO0TlewQRuOKJLAkCtuOyVMmxUaniukmU0AjTl1+gVlgiGushJHgnnuW859L90PhDmLbJzOYMP3z4hxlMDvLCzAtMZaaQRIlH9zzKQ2MP3Xmwp6RcdaW+EVzbaz3e6r2JIoNmi69lLlFpFAk4DlFFxRUcZhoaKi0+7VshpF11il2rbGIa6wQDj9EpTLLl2EbTaPL26tt0hDo4OnT0jt6KKogc9YXpkzXWLJ2G6xASJHoUjZSkfCw9VGCHqHwocXzkOF8+/WUu5y4zkZ7Y1tPM1rLIksyD49vHXY8MHuEHD/4g/+fT/6fXIjLq7RX0vp59PL73cRbyC8zn5hnrmuB0q8qqaZC+poJiuy4Zy+TNZpX7AtG2Y2PWMQlJMj8U7qDh2tiAikBYlKm7FtNugC5t97Zgr/eMpSV47TVPj2K1gLchrMLEMejtgIMHvSmdahUaa9AxAIcfhg+ivK/uAv0iOJ75XkANIWxJhhV0bFdCd3wgCLhCGFeooyhhosFe5MCxGxq+nV46zW8/+9usl9bb+pBcNccfvPgHnF89zz997J/eFbJSa9VYzi+3pyeutD/8qp9upZuVwgqGZfAjR35kWzRDw3GYL2dYWjuPJi4R9FuYbpBGs0S1VSUZMjF9nZTFflL2JdaNecxAApV3OUFWV7cuCF1g92ydeaqeXbgrg9UHdR/rkQ5s18bvNMFWQDTAqYEUIYhLAYEMMknZT8tqoYk3eV1JQRFA3wqk28EHjy5JIVNZRZIk4oJCo5ZjtZIhpoWQJZV6l8Da/KsIrkPTaGDYFpaqIQgCftVPw2jQn+hnpbgCwKGBQxwaOPT+dyw+7p0fzAYo76g4uI6nXRn67LtuZurtr1HfnCMx9iC24kcXLCpSBNnWSRVfpld+E8d3FFEM0DKrmM05kFJUhL1wze/Dr/qJ+qNcXL/Ivt59bYH77UISBHoUjZ47fN5HGTtE5UOIWCDGj5/4cX73ud/lzMoZksEkiqSQr3tOmJ/d91nuHb73uuft6tzF43sex3ZtSs0SiqAw0DHASGrE6wG3qlzeuEy0Y4R106RLVrbN20uCQLessGzprFsGu7YuYEtGC/9WJoaP7f3PsCBTNHWytvn+icraGvz1X3si2d5eEGtQnoaqDKfPAFsjy+GwdzNj0MxBqwCBD8B8SxkC30FovUVUUumJROgIRmjoeQzbRre7MR0J0zaotqr4lQihYA+Tw1+A4MPXbc6wDP7rq/+VzbI3nnit70lDb/D6wut88/w3+dKxL72v3S43ynzr/Ld4afYlHMfh/Op5uqJdbTdb27HRbZ2G2bgum6beqjK3eRlch8GARFUKoTkaqqxRdVwut0wESSQkhHHdAKJdvslevAOO6WmQBAmECDhRbwlunANEL2DSdmkgIOPSchVEwVup4jYBj7ypuNQFEUSFpOpjoVW/8etZOroL0dCOKdv3BKVZejZOoW+epdkqErF1DpkCvtg4dWmMkqzhmg06Y304VgvX0mk2iii+fvo7+4n4ItTKOdTiOXa1WjB7HiIhGNwHI8cgeIeGlZblVWUbDRATEN4DxbMeaVG2Jttsw2v5BHuh79Y+R5lyhjcW32BAUkhmzlPzJ7AkmX51hVFnikqpyhkHRlIiyQC0bImSEUAIfIKme715ZCwQY7OySaVVIaXsHKPvhh2i8iHFidETJAIJvjv9XU4tnsJxHQ4PHOah8Yd4YPQB5Bu0OVpmi2QoyWBykGw1y2Z1k3KzzOWNy3RFu1AkhYbZoGCbuHBDUyBREPALYpuouK6LjntLAyEBtiWQvie4Lpw543mjjI15jrMb69DIgZACvQmXL0NX19UsH1EGx/ZWRR8EBMnT2khJfPp59qYiXMzEKbZ0MnWdYquKJDbbmTqJUIIjA0e4d+R6EglwZukMc9k5RlIj11muB7QAUX+U5y89z1MHn7qtPBPLttAtHVVSQZIo2BbZWoFXZ57n/NyrWI5Fb6wX27VZKix5LcBYD6qk0hHsYCA5QDwY37bNbGEJq1Yg1DGIxDoiDiYCVSFASQlSsl0ilkFFlnnL6STtBrBdl3crqOBLbJGVkHfhsPMgpvBOQS7YJoYvTd6Gi/QTEBKIAYd4c4lOZK7UmEzAv/V9j3QMcSZXpqzXiW6zzHfIllaIxIcY6trzrp/jBwHbdWm6DgIQEMSPbQYL4HkfzX4FLXeRgUaGdTSKokxHs8x+p84MNlj95IwG4XgPdm2VtBSgLIkMJYeIBqL46i0mz17mIDmG0ynI1mDNgrnXYfFV+MSXoPPg7e3P0hK8+qq38LFtb9Q91AX+IggLXosavN93pB/2/RREby3Ez9Vy1PQaI8kRVMsgUc0AoMpVtECLvpDDuU2XDXOQpH8Ax5ymaJdx7QkvnuMduJK/9YH6Fn2M8IERlYWFBf7tv/23fPe73yWTydDT08Pf+3t/j1/+5V9GvcYP4+zZs/z8z/88r7/+OqlUil/4hV/gX/yLf/FB7dZHCru7d7O7ezeW5Yke361EGPVHqet13lx4k/XKuqdCF2VWrBXmsnME1ACf2feZLc/D64mF6bpUHIuibeNgYLoOiiASEiU2LOOGr+m4Xo6o9n5/cOWyd4JJpyGfh3PnoJSBSN6bnHE0L8tnbMxzjwWvZKuFQf0A/UkEGXwHQNvDwd2PkucF6nwXKzuHX2vRNJsApMIpjg4e5Yv3fPG6i/8VZGtZLMci5LveqwS8VVaxWaTQKNCj3jy6vaE3mMpM8ebimxQaRcxAgnjvfsKRLmZzc+Qdl9jQJwhaBgE1wJ7uPQwkBljML9Id7WY8PU7TaJIMJa8T8xXqBRK2jo1LkTRhZ45FKUWJEIILPiziVo64EAB0GkKct/U6x3zhW1+MI4PgS0JjA3wj0CqBlQcxCI1VTDHI6VAfGaKYQgQJEDU/62aCohtgFJEALhLQZdaguUnv7qe4x3eakyvnKPvixIMxXKtFrrKO4Ivz0N4fbmdWfa9gOg5vFld4q7RG0Wjhk1VGQh0cS/TRdxuW7x85WC2Y+XPPRK2yTEAvMWQ16RAkssj06VlGa6sYSoK/lDoJaCWCXasYRhUjLKEEUjQdHwPn5uhrLiIOdODrOwZXiGe5DJcXwP9X8IkOCPfecndYX79ale3rA03zqiubm5Abh4FHIGV5bchgD3QdB9+7t1r9ih9FVGiazW2LiKoV5nJjlKS4SEzdICzlwIkSDB2kLCSo1BzSW5sv1ouYtknMH2vrBuOBG58rdrAdHxhRmZqawnEcfud3foddu3Zx7tw5fuZnfoZ6vc5v/MZvAFCpVHjiiSd47LHH+C//5b/w9ttv81M/9VPEYjF+9md/9oPatY8cZFlGvo2vqj/eT7aWZaW4wljn2DZDoWw1y0J+AUVWCIsyNh7JEAUB14VN22TV1Gm4NkXboktSebZeYlIL0q9orFo6uutcR0hKjkVElEhLEphr3miya3g26PLgu5qc6abOfG6e+Yuvkbz0EkokwcBqkagtIUdSYOTBXYKq6oUUvvIyfO7zgO21ffoevL7v/EFAUNB8XTy293MMd+7nmalnmMpMYTkW6Uia/b37OdR/iOHU8E03oSmaVzzYSjZ+JwzLQBEVfPLNqyl1vc5XznyF56efp2oa6PFeWkoIce5V+mQNv6zSFU7TUjTcrt3kNmdJ+8MEtSDdsW6MLfKSr+d5oPuB6/ZDkRR8Rp2EpZOVIlTEFDUU6oJKQNAJWjUCcgvNaRL2DaJqKdYtg7JjE7tV60+LQO8DsPyMpy+SB8FY8Dwu7DIrnQPMK0OMKAFsvcm6LhCVdRL+GCVT5WKrQQ8Ge4wiSbsGqUOIfQ9xb+8DJKa+zoWlN8jWNhBEhdHBE0zu/gzDvbe5Ar9LMG2bP1l4g9dLa7hWi4AgkrMt5kurXKxk+EL/QXbfhQTpDxXWT8LGm15ls5UDq4kkKoRcB1nPYiKjOzaWU+IzHassihaoSRYMA4EGfdosemGVcDaHnFTpTh0hcG11LBqFZgMyy1CceXeicvbs1arsFciyt7hZXYVlC0586fbywK7BcMcw4+lxzq6evW4hUrXCvJlRGE1+glTPz4EaRZM6GO15i2enn+Xi6kXOrp5lpbiCjU1QDTLeOc4vf/aXd8IsbxMfGFH59Kc/zac//en23yMjI0xPT/Of//N/bhOVP/7jP8YwDH7/938fVVXZu3cvp0+f5t//+3+/Q1TeA2zXxqf4kEW5nYAsCAL1Vh3DNuiN91JulklLCnFRJmubJIB1o86i7aDJCioCnZLCLs1P3bF5s1XlHl+YIcXHnNEiKHoVFsd1KTk2InBIUwg0nwVjypviuKL/l5Lgv88zLroBmkaTpy8+zVRmiqAJQVnAvXyRtY0ChXQ3Q7ObKK0GdGvgr4NpwPQpeFmDyV2QnITUoe/Z5wveqPVE9wQT3RPYjudFImzleLxbeX9/736SoSTr5XX64n3X3Z8pZ3h096MkQje3hH917lW+8fY3McKdKL3DuLF+VMDWa5zPLxDQazyR6CcI1BKDFGtF1ktLRP1RZFFms7rJVGaKowNH2d21+7rt98R6EASBsNEgImvU7TAt1aCXOUJOA9Mo0OXrIu2bQPUdBFEhb+pUHOvWRAUgMQ5qCArTUFn0JsTiJ3D8BouSRLVUZyV3idXCJkvNFkVLQ4sMk0wMkkj0cFD1cSDShRjph1APiBJiuIeJY/+Q8X1foN4sIch+guH030ir5aXMFK8WlkjLGpFrtDGmbbBUWOHrokL/6CcIfJx8XQoXwTahlQej7n2/kooECK4LehnXrGB2hOhQDZ43duHodWQ5Rio0hqz56fZPEe126OjYRVfsBpXEYAgqZSjNQf/DN3ctrlQ8+4LOm4wud3Z692cynvfS7b7FWoFio8jxkePMZmeZykwxlBzCp/gwLIPlwjK26/Dwnh/FF9jVft6hgUP82Vt/xp+d+jOaZpOQ6p0jcrUc1VaV33/59/m19K/h1z5+IYJ3G99TjUq5XCaRuHoSfuWVV3jooYe2tYKefPJJfv3Xf51isUg8fn1ZTNd1dF1v/12pVK57zMcRtmOzUvRaOJVWhbAWZrhjmP5EvxcwZzS9VN3cIpIgka1mydfzJIIJuiPd9Cf6CfvCbFQ2cK0WQ67FX869xtT6RTZbVQRZozs9znDvXibCKeJbF501U2fBbHGPL0RMlFk0W5RtG0GAmOsQbFWxCq+yKVwiFd2DcCUd2XXAzkDjGa+8r1w/IvrG4htcWL/AaGoUVVaRdlv0nF2gFYySXZvD7wToSA9TtTsRCmuQFvBJDursBuqBz8PgJ0H+m1O+S6J0XYL1rdAZ6eTRPY/y52/8OeD52kiiRMtoMZebIxlK8sS+J276/Ibe4MWZF6n5Y0R79oEoIbk2qtkCUaIZSLBZy7NRK9AVSpLWQgT6DyDiUKhuUtfriIhMdk8y0T2BaZvXObf2x/sZ6Rjh0sYlBpODBNUwkqMg2wrlep2If5jO2P2oWs/15lm3g1CPd3MsT5ckKRiOzanZF3jj8rdpGDUsN4LhqIiWRXNjhmqzgipKLATjNPuO39DZU/THCfv/5srotmPzem4BVZSIvMPLQpFUOrUAS7Ucs7UC+z8uqctWyxOy44Je8Y4HyTuX2y44ih+sFhJNZNlBb8WIRhKsOxCRLEJaCFEU8cs9DPS5RLRbJWe7NzeEvALT9G43q5YoiifaNm/Pfn+lsMJfnf0rXp9/nYbRQJVUQr4Q1VaVmY0ZHNdBEAS6o908dfApHp98fNvz57PzvDTzEnF/nN3p3Tg4SIJEUA1Sbpb57tR3+fLpL/Njx3/stvbn+xnfM6Jy+fJlfuu3fqtdTQHIZDIMD28vlae3IuYzmcwNicqv/dqv8Su/8isf7M5+yGBaJi/OvMiZ1TO4rotP8dE0mrww8wIxf4y+eB+XNi4xn5vHdExGUiN02p3kq3lUSWWkY4TueDflpjehsVpc5U9f/xPeXH6bNbNOzjKQXFi89BzFnkl6jnyBdN9+BEEgLnmVl5brMq4FGFZ9VE2DsytnmV09R7m+its6g18NMpQUODE0QTwQ8k5aco+Xk6NfvI6o1Fo1LmUu0RnubOskKuNDmNEQgdUMEd1gPRGiYRnI1SZ2sJtSfAy5VMAK9jO+4RKV7r5J2geNv33sb+M6Ls9MP8PbK2+3498HkgP8nU/8nW2J2O9EtVVltriC2jWJ4lg4ruvZkQC4Dh1agJysMldepyuURHJdAv4IuwePYraqvDT7EpqssVpaZbW0StgXZnfXbo4OHCWwpZ9QZIVHJx5FEiXmsnPotkEh1EFLDTIY2M9ozz60a3r6dcfGJ4pE78D1GPCE0Hii0zO1Ii8vnKHhqATDY5TqeXzRLoJA0GxRKq8jmi1W80ucnDvJY5OP3dlrfQ9QbpTJGg0i6o31RyEtyHqjxGajBB8XouK6oMWu/i3gLVAEEQsHy3XxCQKCP4wit2jIHYwlBhkZuAfBMhkURXySTKJlEmx9C6qmJ7gW3+HYWq1CtwqJXbfOgAoGvRiNWs0LG30nGg1PsxIMXn/fO7BSWOE3//o3mc5Mk46m6Yp20dAbrBZX6Qh18OThJ0mFUwS0AEf6j9zQUuAb575BtpZlsmvyuuGHjnAH2VqWb537Fn/r2N/acU9+F9wxUfmX//Jf8uu//uu3fMzFixeZmLha7l9dXeXTn/40X/ziF/mZn/mZO9/La/Cv/tW/4p/9s3/W/rtSqdB/tx1J7wCWbbFUWGJ2c5aqXiWshRntHGUgMXDj0Kn3gLMrZ3lj6Q364/0EtSCO4zCzOcNSfonXK6+TCCUo1osEfUGaRhPDNAj7w4S0EJvVTS5tXqIj0kG+lqcv1scfn/xjXr78MqVGiVyzSMsFyXUxRZEzlQyOXuNHHvyHDKZ302hWKboWVUkhEoiiCCIXl97kjdlXSAQTjCYTiK0INTvBhY1lanqTz07eQ+hKOVNMerHlTtOzlt5CuVmm0qpsizq3wkFy9x4i+Z0XkQoGLUTCjonU3YOVThNSNVQbZtIxphpzHM2cQQ5EIdTbXsndKSq2Rd62cHAJihIdkoLpOtiAJgi3FZduWiaVVoWG0cBxHWRRJhlK3jBnRpVVfvz+H+fxvY9zdvksuqWTDCU5MnCkTRZuBUPx4ygasmVgySq44CIg4CKKEn5fmKqlU2wWUQJxFBcEx+KtpbfYKG9wz9A9DCYHEQWRUqPEK7OvUG6UeXzy8bZYO+KP8Nn9n2WttEauliPruiwoAXqCCSLK1c/ZcB3ytsUu1feeR9NXLZ3X8nPIjSKxSJpKZZOAKOIDWoKIoPgQ1QCVwiLHuyaYzc5ypH7kQ5eYLAgCsutg3+JC6rou0sdp+Ef2QXgA1CgoPjAVMGrYSgDTsZFtHVdSkCQFQRYJdozRkxrDEUXytkVcC9IhKWBXoLMflssQXQN/rydid10oFkGpQ89hiL+L0ZvPBxMT8MILEI9vN4B0XW8KaHT05q2ha/DVM19lemOag/0H2+fxsC9MKpzi3Oo5Lm1c4u8e/7s3nL68gpXiCrIo3/QxQTXIenkd3dRv67f//Yw7Prv84i/+Ij/xEz9xy8eMjFyNoF9bW+PRRx/lvvvu43d/93e3Pa6rq4uNjY1t/3fl766uG8/Na5qGpn04jG50U+e5S89xfu08Ap5p0eWNy/zFW39B02wS8UfojnRzfOQ4943ed1sjp+CN+mYsgxVTp2y2eCO/iC+SxrclMlsrrTGVmSLij+DXvNccSAwQC8Q4VTvF9MY0h/oPIUsyEV+E5eIybyy8Qcwfw7ANziydwbANis0iiUCcpighAq1WlZbRYD4zxXdPf5WRwaMUbQsH0M0m+7vHGYgP8PbK23RGOj3FurlEsdlgrWGzWS1zanUWw7b4zJ6jJIMRQNwaH94+QnxlNM9xHSSuriYqE6P4L1xiIxJgubeLoNaBG4iA66KtZxBDNsPuOZqGSX2hSlT1QSAN3fd6YXW3CdN1uKg3WDBbNBxvjLTl2FiuS1CS8W95xgwoGsOqH98NRG+WbXFh/QKnl05zZuUMG5UNJFGiM9TJWHqMwwOHOTxw+IaEtSfWQ8+N+vG3QEeog45IJ/NGA0ENIFsGsm1gyiqqpdM0W0RDSfqj3Vhmi81aiY5mgQuVDPlanuMjx5novrqAuBKQNpWZYlfnrm33iaJIX6KPvkSfl7FiNJkyGiyZOpogYLkALoOKj303SFy+HTiuy4LRwjZaRCUZQxBp4BISFc8e3HWoCyKKFkTVa0QVjaXKBpVm5UNHVCL+CP2Kj4tWi4QU4p1HS9FoEpJVRoIfo1RnQYDkHogOeEGTouppj8waouNNFVq+DiwxhKuoOOEwrii1J9mtK7YCdhb2PAp5G+a/A6WLIAlg2uBXYfchOPRFCNyGbf7+/d7kz8yMl64eDIKuQy7nEZTjx2+YknwtctUcr8+/Tk+057rfriiKDKeGubRxiQvrFzjQf+Cm2wmoAWzXxnXdG2qmDNtAU7S7tqD9OOOOP6FUKkUqdXuly9XVVR599FGOHj3KH/zBH1yncD5x4gS//Mu/jGmaKIpX7vv2t7/N7t27b9j2+bDh9LJ3geqJ9VBr1VjMLfLy7MusV9YBTym+XFjm1flXOTl3kv/hk/8DYf+tp2CuBMItmC0kBFp6jQ3HJRLpYQmHXqPFYmERWZQJ+ULopudCi+BNlexO72Y+N89SYYm6XqfcLLNZ3fQuVEPHOTl3kqpepVAvEPaF8csalmOjuw5+LYTtOuSrm5xZfAMllCDRd4heW0cx67w48yJ+1U+tWaM37qnvl0tlzi+v0rRlAmoAXPjm1FsYtsWju/YzHNFB7vXC/a5BR6iDZChJtprddsFudnVQOLqP2ouv0ZUrEeyIITULSPU6YsCEIQfJsqgmJ6nGR4iKopflsfhtED/r+SLcBi7oDS7qDRKSTFKWqTg2a6ZOzraI2zZ7tACm4HBar5F3TO7xRbaRFcdxeGHmBf76/F8zszFDqVnCJ/swHZPLm5c5v3aeF2Ze4FN7PsWPfeLHUJX336aSJIkHhj7B3NIpis0KUV+IgF6j6o9QE0QM2yIW7mK4ex+dsszeVoUx12Y9v4gsyduIyBWosooma0xvTN/wfvBI5bgWICUrZEyDmmOjiCJpSaFTVt+zbbfuOlQci4iiIbsQFyXWHQdDErG2Klku4DObJAAB4UPnPWFYBtlqFtuxmQynuJydZUPRSIoqKt7oftWxyJotTsR76b/J6PpHFtERGP4MVNc84iL5obGJbRvoviiiEsZWIpQ7HkQMGqj2Oi2xAwRQsMFcAtEHkRPwQz2wcBTmT0F9HcIhGJqEgYO3b0EQCsGTT3rGkNPT3nizqsKxY14Ke8etdDAe8rU8daNOb+zGE0ZhX5iW1aLQKNxyO/eN3sdfnv5LSvUS8dD2792yLBpGg/t33f+B5Hx93PCBUbnV1VUeeeQRBgcH+Y3f+A2y2Wz7vivVkh/7sR/jV37lV/jpn/5pfumXfolz587xH//jf+Q//If/8EHt1l1DQ29wYe0CQTXI1NoUa2WvynHFtlwQBBpGg0N9h9AtnRcvv0hXtIuffOAnb7ndy3qTWaNJt6yiCiJFUSJotQg5QXKKD8f1yMcVLw5B8E7elu1lTMQCMSK+CLIo0zAaBH1BBtVBHhx/kI5gB3956i/JVrOYtolf9SMIAgFRwnZcDNfFdlx0y8CwHVx/jBTQ7bqoWyGJL8682C5llptlzm+sIEgRev06SEGkrSTnut7iuZk3SE4O0FQPsVo8jWEZBLUgg8lBIv4IB3oP8J2L3/G8O7ZWyC4wP9nPMgaHFysIuovj89MYGSAQmENw6pj+tFfaBS/HIzIIpVnInYVw36372EDZtlg0WyQkuT3BtGS2sHEZUTSyjknVtRmQNSKizLKpk5Ka7L5mbHJmY4Y/feNPyZQzrBRWsF2bfC2P4zhE/BEs2yKgBvjK6a/gV/z8yJEfefe01NvAZ3fdz+lGkQv5JVrlDRRJwapsYvkjBCKddKSG6PYFOOYLM5zoxS9KvGo0bzny7Ff9VFvVd33tuKQQl97/e7gC0aMeJKJdhANRMJt0iyKFRolQIIILGK6A2CzT1z1BsVkkGUreWSDdBwTHcbi4fpHTy6fJ1rI4joNf8dPlmGSNFku+MKKsYTs2qmNxLJziRwaPfPyM3wQBeh8EOQAzfwmVJaRQD7ZrU7FMlGCa8uATVHoewG9fJqifwjXnSSEQlYIgd4H/uBc3ATA24d3eD4JBr3Jy+LCXF6aqN9as3OzpWhBFVmhaTUJcXy1smS0kUSKo3Frr8tD4QxwbOsaLMy9iORbJcBJREKm2qizkFhhIDPCjR370jt/e9yM+MKLy7W9/m8uXL3P58mX6+raPYrpbLqbRaJS//uu/5ud//uc5evQoHR0d/Jt/828+EqPJxUaRcrNMoV5gpbhC2Bem1qqRCCU8xm222Kxskqlk6E/00xHu4OXLL/O5w5+7qSFYy3FYNFtERbmdvxPWQoR9IRrNMn5JpSCp1C2DStObdmqZLSK+CIbt/V+2muVy9jIAiWCCgBIgGUwy2uFN1gx2DHJp8xKWbeE4XulVFgQiokzLsSmZTQTZhybJ9OEwZLVQtszhVFklqAVZKa7guA7r5XUaepO++B4v/dYu0NRbdIaiDERsLm5u8ueXEjTc0zQMz8HVdmziwTgnRk6wr3cfTbPJ6eXTXFy/iCzK2I5NxB9h4tEfpFa2yM0vg6GjukXCzTyt6CD2wBCC5iN4bYUukPKi3PUy+GK3/O4KtknDcejYquJVHJuKYxMRZRCELf8Lkz5ZQxYEwqLEoqkzovrbmpW/OvtXHkFxbBpmg1KjRMNo4LouVaOKr+ZDFERigRgvzLzAgf4D7Ol+/y6pEV+Qn937BH+xep6ZwjJWo4QiSiRjPSSTgxyM9XI8GCF0jbjVr/qxnZuXoBtGg67IHVqU3wVookinrLDgBBjvO8jpyy+hiRKibWK1qgiiRLVeYCwQJ6gGqTarPDrx6A21P99rnFk5w3OXnsMn+zw9mihTaVWoltYYlTW6wimqjkVA8TOZ6GfX1nTexxKCAF33QGyXt2CoLOJ3TMpqgvlAL4FgmoAgUpTGWNR6CMgZ9mkyshIDpReEu0d+t0FV79gvBaAv0cdk1ySvzr1K6gYRDMuFZfrifezt3XvL7WiKxr/94X/Lr/7Vr/L64utkVj03W0VSmOye5F98+l+wK73rltvYgYcP7JfzEz/xE++qZQE4cOAAL7zwwge1Gx8YBEGgZtTIlDMkQ0mKjWLbwh68gxE8N8K+eB+d4U5mN2dZLizflKjUHJu6a5O+RhgqSzIDiUHOrJxGaVVZL62yUs7QrBdQZZV8LU/EH6FQL3Bx/SKq5CXZyoKMYRss5BYIa2FEQUQSJXaldvGq9mrbElqVVU9x7joYepWgJCOJMOQPs9sfbZOUK+iN95IpZ1grrVGsF71AO8EP6i4MPYvNIr3RTqp2B5dKFc4Vlnh8coKBpLdicl2XjeoGz0w/g1/1c3zkOLs6d7FcWKZltvCrfi/t1hfmpXqZXE8vyVIVobYExQxOz16KgktclD1icQWi6o28OhbvBpvtju+m6+K4HmEDb6Xvui4OLiJepEDddTBcF0WAfHWFavkNdscMzqwvg12nZbbwyT5kScawDAzHoNQs4biOF0C2dpGJrom7sqIeCcb5qdHjnE+PsaTXcBFI+aOMagEGFK1Ncq+gP9HfPkbe6dSqmzqmbd55Iu1dwpDiY80yiKcnOITA7No5qs0KmfIGuiiRVgMkfSEkUeK+0fs40HdzTcD3CtVWlbcW3yKshbdVd6L+KEE1yMzmDJ8IJTg8cPhvcC//BuCLQddR6DqKBkw4NpLRZHkrC0xCICGH2BXY/6EP1PvBAz/I5c3LnFs9x1ByiJAvRMtssVxcxnZsfujAD92WADYdTfNbP/ZbvLn0Jm8uvIllW4ykRnhs4rFtthw7uDU+phT/g0cymEQVVYqNIulommKjCAJtceiVNodhG5i26c3ci8ItnQgFwLEdTq+dYbO8joDArvQYI6lhWmaTk5df5uLqWSKyRt5oUGwWCapBJFFiqbCEbdsEtSBRfxQb2/NaSQ5juRbLhWV2pXfRG+9lonuC00unEQWRbDWLJHr7azu2lxvjOOxOj3uak2vgui6mbbKvZx+u67JZ2cR0TIJmkIbRoGG4pBPHWNMV1jfXeXP5Eh2hDs6snmG0Y5T+RD+CINAV6WIuO8eFtQsMdwyTDCVvaHV+2B/mjCCwHAoRbilgn0e3asS1KCOqf7s2wqh6DrXvUo6FrewVBCzXRRYEbxJDcNtOvbrrEpUkrsj+TNdFRkBGAGMWvfBn7IvOc1Yvk9YqpJJNIrJFphXAdgVkScZ0TGzHxsGhZbbIVrMYlnHHSak3Q0JSeDCapuWksHHRBBFZEGiZLVaqORzX8dqA/giJYIKjg0d5ceZFmmaTjlAHkih5U1+1HPt792+bvvpeIiWrHPaFON+q4+/azURykHR1A9NoEXNs9viCBBWNvnjfh0ZAu1Jcodgo3pDcyZLcTsa9Nnjy+xF+UeKAL8SY6qfpOogIREQJ8SPQ/jo4cJB/8sg/4U/f+FPms/MYjoEsyvTEevjBAz/Ik/uevO1tiaLIsaFjHBs69gHu8ccbO0TlPUJTNHZ17uKFmRdoGA0SwQRBNUhdrxPUgpi2SSKYaAv/MuUM3ZFuRjpuPpmysHaBPz71l6zXixj1HAB+xceuzjG+eOSLBFQ/veEUfeFOmo0iNb1GQAtQ1+u09BYI4OISD8bxq372dO1BlmSK9SLLpWWGOoYIqAEmuycJaSHWy+ss5hYpNorYjk1ACxD2hVEkhdXiKufXzhP1R3Ech5bVYrXo+W8cH/aqILqlewJbxU9ICzGQGGCjssFmcxPXcdufkW7qnF4+jeV4qwnwxLSrpVWqrepNTdP6FI2YJLNu6pRkjWh8lGRljlA4jXKt74BjXmOn/+696JSskJRkNm2DbkklLEoEBYm6a+NDxMYlJasIgkfOSo7NhBZAszNQfxrBrVF1u5ktVUGKUdfrDEdMRKHObFnFcZ32RJMqqjg4VPXqB+KVcEXgazs2p5bPcnblLIV6Add1ifgijKXHODZ0zBt/VgO8vfo2mXIG13UJ+8I8PP4wB/sP/o0K+gYUHylJIWOZNLUgcriDDtlzT/6waTpsx2Yxt8hqaRVREAlqQVKh1Db9kV/x0zJbWI6FKu6smv2ihJ+Pnk/IsZFjHBw4yNmVs5SbZQJqgAO9Bwj63n0xtIO7ix2i8j5wYvQEL11+iXwtjyiIRP1RLm9exnZsuqJdyKJM2BemWC9SbVX5wtEv3LRcuJBd4P/z5V9m0TZIjz1MIJQEs0m1VeHsyhmqpk5C0TjRt49maZ3h5DA+1cfF9YvUWjUUWcGv+kkGk+RqOWL+mDfGGUp4BnFmE9M2MW2TgBbgnz/xz9mobPBHr/wRK/kVkuEkneFOxtPjrBRW+M7Udzi3do7B5CDFRpG6XiekhdjbsxdZlDm7cpb+eD+fmvgUxWaR8c5xFguLFOoF0pE0s5uz+BV/m6jJosxsdpaeWA8+xddOHHbeJfk4JEqMKT6oznkZQo1NaGQgsRdUP5h1L+AuPgap22sLKILIfl+QN5tVViyDsCgRFWVmzCYCMKr4SYoKhuuQtSxiosyQ4oPWq+DU8AXGifpX8cl+qq0yDdNFt2ySWoMZ16Sme1EGtmOTDCVxbIfB5OAHplFwXZdX517lldlXiAaiDCWHEEXPJ+W1+deotqo8Pvk4kz2TjKfHydfzbSLzYfFv8IsSw+qH82JWswymCkvMb1zm4tKbLOXmvapKvYgkSsQDcfb17SO5NXpcN+okg0nkOzXB28GHDqqscs/QPX/Tu/F9j51f0vtAV7SLJ/Y+wUuzL+FTfAwkPS+TtdIa+VoeFxfT8eyanzr4FE8deOqm2/rL03/JbHaWiZ49UM9iRbpwtSChYAIp2sNSYZmwopGM9/JmZYOQP0SpUUIURIY7hlkrrQEQD8ZpGS0M22CxsIiDt7p3XZd8LU/NqHGw72C71D/RNcGnJj6FLMloskahXmC9sk5QC5KpZJjOTLdJWEALMJwaZjg1jGEZ3v52TZA0kyzkFzi1dArTMclWsuiOTq6W45mpZxAlkaAaJOqLMtY5xlDHEMVGkZg/RujdPDhqGXj79yB/FswGWIZXQSkvQM8Jz4594Kjn53AH4YSdssqJQJRls8WqaRCXZA5IQQzXRUJgzdKRBIG0rLBXCxITTDAXQUoSU6MMJvpJBmfJ1gte68xWiKomMdVBtxQ0WfOqKjjEg3H29+2/7X27U2SrWc4unyUdSRMLxHAcB9d1SQQThLQQ05nptk+KLMmkI+kPbF8+TjBdh1OVHF+7+B3mNmZY3piiUS8Q18JYloVlW/TEetisbnJm6QzHR46jyip1vc4Dux74vm777GAHdxM7ROV9QBAEHhh7ANM2mdmcwS/7+eTuT7JQWCBbydIZ6WRP1x564j3E/DFOLZ+iM9JJb6z3utX1czPPEdACqJKCW15HbpSx/WFcUSHimKwuvUEzOUSpMeppRSyTfC1PSAuhyAqiKGJaJqqsYtgGfZE+JElCkzUW84sMJAYIaAGODR/jQN8BFFlhs7qJKIjtUWfHcXhl9hWmMlME1AARX4RcNUdHqINUOEUilGCluMJgchBVVkmFU+SqOT53+HOsllYpbAl8F/OLbJQ22p9RQA3QMBpsVDZ4YeYF4oE4DaPBA7seuPo52CXP+AkXxDhIHWA24fRvw8ZbEB2G2JYIuVWE8iw0srD/Z+BmGS/5vOdGaZreyOLAwLYxxZgkE5NC7NE84ayCgIlL3jK3nGlFkpLs9dQd3YuGF3wICEz2TDK2colz6/MEtCiq3cSv6oR9CqYQvLqaduHxycfZnb4+APBuoKE3eHn2ZWazs/Ql+lgtrbJZ3fTGpH0ReuPesTazMXNTn5QdXA/HdTnTrPIXU0+T3ZghpQUoiQqdXbsxkCiaOufWzrNaXicU6mCxksWSNbqCMfZ3T7Krc2eaYwc7uFvYISrvE0EtyJN7n2SyZ5Kl/BJNs8knRj7BcMcwtmN7Md/rF3FdF9u125M3j+x+hOg1ke8NvYGylXEhAILZQDQb7ftlo0VYC2HZFoqksFzZoGw2iQbiCK6LKqqIikjLaIHrVVYaRoOIL8Kn9nyKT+35FMMdw+3RTsdxsGyrPSoOsFhY5ML6BcK+MLFADNv1dCu98V7KjTKSJHmajUaJ1JavykphBcM2mOyZZH/ffi6uXWQ2O0s8EKcv0UemnKGu11FlFUVWOLtylt5YL08dfMob13V0aJ0EfQqcLS8PMQDKCORNyJ71UpKv9QHxxUHeB8VpL2J+6B3ZL5YFr74K5855OSGi6Flod3bCiRPbI+C5Mu3jaSFUBLpvJHgV/CDFwcqCFCGgBjg6sJul4jqLxTLlpovlCgR9KURZQ1M0VFllrHOMz+7/7F3XWriuy9mVs7y1+BZvLb7Fpc1LvDb/GoIgMJAYIBlKkqlkWCuvEQ/E222JHdwecrbJ6cIy9cISA8l+ssU1HBwCskazsknFbFK2dKq1HGKzhGnq5IwGX3rknzA+9oA3DbeDHezgrmCHqNwFKLLCSGqkLRQFqDQrfPX0V8nVciSCCWY2ZpjJztAyWjw79SwXMxf5nz75P+HTvAvwQHyAV0qv3HD7tm1jORZ7e/cyMXSE02cyzFU2qDZKlCwTWRLpSvTRqYVZzM8jiRLlVpmW0WIwOcgPHPgBj3g4NrVWjZnNGV6ceZHTy6e5tHGJia4J9vXuYzG3iO3Y7XaMZVv4Fc8ULuKPUG1V214nV+6XRKl9m+ye5MtvfZmG0Wi7zfZEe6gZNTbKG8T8MSRRIuwL89Duh7ypnfozoJ8GKQ1Kl+fJ4FRBPwuF8yC620nKFcg+L/hw863ricobb8DJk5BKeQ6V3ofoWWt/97teVeUd3j7vCkECbRLMb4LTADGAIikc7R/l2GCATHGKmaJJXYrg14Ikg0kCWoD9vfvpCL+7G+ad4sLaBZ6ZfsYz0OsY5O3Vt9uj5oV6gXgwTjqSpmk0ubx5mfHO8XfdZrVVpdQoIeAlwkrSh1Mz8r3AhmVQrGVxLRO/GsB1HQQEao0ym8VVTMAX6SYiyQx0jFBpFDBtAwuXi7ZD1NRvTHh3sIMd3DF2iMoHhLnsHOvldSL+CF8981VWi6vIkowsytSNOv/3yf8b27H5pSd/CVmWeWLfE5xcOEm+micZ3r76veK98sjkp8lHuziuRIgGk7x49que42GsH1P2U3YtDvUfoivaxUJ+Ab/ip9wo87vP/W57QmG5tMxyfplqq0rDaJCtZVm9uMq51XOEtBA+xYdu6e1qDK5X7Qn7w7QaLWzHbo/YZqtee+vK2Ojurt1osoZhGe1YdMd1cByH7mg3w6lh8vU8lusRHMwVMC6CPOBVUa5ADIMyCu53QLtFJULSPDHttahWvUpKMnnVvRZAkjxyMjMD58/fOVEBUHeDbwP0M2BLJHwu62KZ3ohNX+QgQ/2HeNANtwnczMYMY53v359ktrzBQjWL7dqkfRFGQylOLZ3ynFAjXdRbdUzbJKgFCflCFOtFstUsEV8ESZRwcW+ZOVJpVnhu+jlenn2ZpcISAgI9sR4em3yMR3Y/8n1p8a27DhK0Py+/L4TrOBRrOWxsBCWIaLWQRQG/5qdhqHQl+qjkFyn15JiXFbpk9UM3tbSDHXwUsUNUPiDM5+bRFI3npp9jtbhKT6xn2wl/Kb/Et859iyMDR/jM/s/wuYOf49W5V/nW+W+Rq+dIBpPYjk2unkOTNX7i3p8gkBrmsl7nQOcwBzt/lpGuMZ4981c4jo3mixCJphmNdHJx6RTrpXVGUiO8vfo2pVYJTfLIxUJugdXyKri0J1NaZovZ7CyyINMV7aLSqpCOpD0PFsdiLjtHXa/T0BskuhJEfBFWiiuUm2XuGbyn3T7yq36ODh1lobAAeNMPImJ7dR/1R1krrRG74hxrrniTPOINRLCCAmoChMs3/5CtBoTfke2TyUCpBKN9UD8L+jy4pqd5CUx4WR/Ly1Cv31bc+/Z9kiHwECh9oE/REVzHpyY5X4oSDh/BFoKIgjcpkClniPqjjHaO3tlrXIOy3uTPFt/kQnWThm0jALIAUduC/CJHeiYBEAWRZChJy2xRaVZQZZVCvUDI57UKxzrHEAUR0zavIx3VVpU/ee1P+O7Ud3Fcx6v+uDCXm+N3nv8dVgor/N17/+5d83/5qCAgSKhaZEv7ZRALJfH7gqzk5/H5I5iui2A0kYJxKo0SqqTQlxqlUi/gNkrkQh3UXYeQ8P1bldrBDu4WdojKBwTLschWsywXl+kIdVx3gQj7whiWwXOXnuPxycdRFIV/97l/x+70br5x7husl9YRRZGjg0f5/KHP89ThH+Y79SIhUWwbJt0z/giRQILplTPUmiXKts1Ls6/gtGrcv+t+NiobqIrKvsQ+anqN56efJ1fPYVhGO9QwqHpmbcVGkVqrRsNoMNE9geN63ikRf4RkMMlcdg7HdQgoAZ6dfpaaXiOkhfjzN/+cb5z/Bnu69nCw/yCdoU4USSGkhUiFU6iSik/xedlHegPHda6O+7k6tzwEY7shuwS1VQi9IyCstu6Zu3Xfu/3/HQeEMhRfBX15S3oigXMemm+BfA/Yu7xW0HuBIIM6Duo4vpDNIEvMTj3DUmaZiD+CIAhUmhWvvTX+EKnw7QV4vhOW4/CnC2/wVnmdLtVPv8/zmmk6FtPlDcqWwbilE5NkRFEkEUzgV/xsVDeotCo09SaiILK/d79HSLExLOO64/Dc6jlevPwiATVAT6ynXQFIhpKsldb4ztR3ONB/4PtuRLNLUemO97IR7SFTWKK3Y4Sh9G7mM9PkK5uUjQayqWO1qlTqRUZ7JlFlDReQEHDwBLk72MEO3j92iMoHhJ5YD9849w10S7/Oq8J1XRzXoSvaxWpxlXw9TzqSRlM1fvbhn+UnTvwEq+VVVEmlJ+5dPCzXxd5ySL0CURSZGDhEX2qYzdIa08VVClaTw6P3Y7s2lzYutUdRXdelbtSpNqvt0C3d1AmqQYJbYXu6qVNulam2qgS0ANPr0zg4KKKCpmqMd46TDCUp1AtE/VEqzQo5M0cz1+Tpi0/jV/wMdQyhyRqnl0+TCqWY6J5AkzU2K5ueWdzIce4d2SIXUhQwPaHrjUrkgQikH4XLFyFXgsCW1qOR8wSyuz4PqX3bnxMKgPC657fiH/Ss9cEjMFYWys9ARwICd8E/RJAYSg3zuUCUmY0ZFvOLOK7D3p697Orc9b7GgKfLGS7UcvSqfsLXmNj5RZmRQIRX1CAXyxuc6BwhHvAM/vyqnz2hPWxUNhAQODF8gnw9z7OXniUeiLNR3mAgMcCxoWPs7dmLaZucnDuJaZt0R7uva1N0Rjq5vHmZl2df5lD/oY9vVs0NEBdl9vjCFIeP0zRbzGcvo8k+NF+Uan4ZcImFO+hNDBDQgrSMBm/Pv0ZXog/BHyYoiPh3xpN3sIO7gu+fM8/3GKOpUYJqEN3UEa4hF67rUm6WCWoeQTAdc9v9AKqqMpzabmkuARFJZsMyeKePa8gfxe+LUBIlhOxl0tE0c9k5XNdtu6Galolt29iujSiKyKKM5Vht3YJhGRi2gU/x4VN8+BU/eTdPvpYn6o9yqP8Q8WCct5beojfeS12vE/aF6Yp2sVHeIFvL0jAaVFtVHtz1IIOJQc6snOGV2VdIhVL0xnt5YvIJ/v6Jv3+VuCkDIEbBzoH8jsqDXQFkGP1pCC/B0nc97xSA1H7ofxT6Hrr+gxczEKhAPQj+a8LORBGEBDhZiOVBvnuHfiKY4PjIcY6PHL9r27xUyaLbJmHf9fH2YS1EzBdiqrTGJzq8JOr+eD8zmzPYfk94PdY5xpuLb/L6wutUWx45nVKnkESJZ6ae4fHJx3l04lHKzTKSKN1QhyKLcruN1DAaN3UQ/jhCEAQmtACBjiF6tCAzuXlWN+cIaUH6Y130pMdwlRABwUWWFBAE5tcukIr3oQSSDCq+doDlDnawg/eHHaLyAaEr2sWn932aV+deZam45I2HuqBbOn7Fz0B8gLXSGgf6DxAPvMMHxLG8i3LxErQKIAcQEuMMBHtZxaXlOG3r9CvI2SZxWaMpeVM5kuiNEl8hIpqqocgKAgKO4wlcRUlEEARM22xP9HRGOjk8cJi53BwRf4TBxCC6pTPeNU7TaKJICufXztMd6aYn1oNlW2xUNwhrYXRTx3EcsrUsj+99nBMjJ3hr6S06I538/eN/n8HU4Pb3KSXAdxyaz4FZAzHpTfLYBcAA3zFQB6F/2IuS10uAC1rcIx43QnkeYiEQgp6Pis8HsgS67lVVYn0g5cFq3Xia6EMCAxuRG7cOBATSoRSbrQrTmWlS4RQ90R5y1Rzz+XkivghnV85ybuUcLauF67pYjkWpWcKv+DFtk2+e/yaKpCAJEqZt3lBoazs2jut4YYvfhy6roiAwpProT/Rxf6yblUQ/nc0Cpc5R3lw6xXJtGhPPINCvBojFutAlhT5RYFj98B5bO9jBRw3ff2ef20S5UWZmYwbLtRjpGNmWknq7+Oz+z/La/Gv89fm/xrItgmqQdCTdTrJVZIVHJx7dlhOCbcLK85A7C4ieDkMvQ3mWnugIu9MnmHZAcQRCooTlulRcm5AgcbhjiOdCHWRrWRLBhJcDZHjW95qkEVAD+BSfd2HCJal500W1Vo2m2cSn+EgGkwS1ID7Fx5GBI+1x10w5gyRKBNQATaOJYRuAJ5htGk2i/iimbSJLMtVWlUqzQiqc4sSuE5TqJcKBq5UBwzI4tXSKTDmDIskcTB+mN1AGa81rA8lp0PZ6WpArq1JRBP9thtKpGuwag0LBIyuODZEopJKgtLxR4w85OpQAriBiOTbyDTKCLFHknu5JTkRSTG9MY1omRwaP8AMHfoCqXuX3Xvg9Iv4IZt0kqAbxq34EBCqtCrlaDkEQOLNyhomuCU6vnKbULF1HmIuNIoIrcKj/0C2t9ltmi6X8EjW9hiRKdEe7SYVTH5uJF0kQiEoym45F06jTNJuE1SDjcR8Vy6DpOLgCBCWNTsdmQpR3qik72MFdxA5ReQdaRos/ePkP+Oa5b7JaXPUu6MEkj+x+hJ975OeIB2/ignoDCILAP338n+KTfbyx9AamZVJulsnXvXbKF49+kftG79v+pPwF2DwN4b7tlvCOhbF5jmBpA4LjrLsugWiaVKSb3WqAAVUjISkU+w7wzNQzKKJCX7yPmY2Ztrlbf7wfURBZzC/SMlsU6gVqeg3b9tpByVCSg30HUSSPOF1pGymS4mUEqQEM20CRFOpG3VtxO45n0y96IXxXTOuuTAIpooLteo8DT7z5hy/9IbPZWSzHAhcigQj7e/bxI4eeIBaI0hEcRJSuIW93gsggiAqIJnR3Q3fXlgZm68KRe9sT4H6IqykAB5L9PJOdZa1ZZuAdqcElq4XoODzQP8En0uMcHTyKaZtosoYsyXz97a8jiVLbYPCK8zBAxBehpte8qITyOveN3sfB/oOcnD+JYRmkQils1yZfy1NsFDnYd5CD/Qdvup/z2XlenH2RzfJmOz08qAbZ07WH+8fu/1iNNmuyxmpxlYbRYLxrvE3EPM2Zy1x2lkZ1g4D8/TUhtYMdfNDYISrXwHEc/vdv/e/8+Zt/jiRKdIQ7kJDIN/L88ck/Zim/xG9+6TdvKz1TN3Vem3+Nmc0Z0tE0Xzz6RcqNMjWjRleki+MjxxnpGNmeB2KbHlFRQ9fl1iyV1pjdXMRsnqIQL1AXfbTWZTq6dzOx+1F8Wxf2A30HMCyD08unEQURnxZkqbSGLEqkoikqesVbadteBpEoiMiKjF/zc8/gPRzsP+jlFF0zsdAyW8QCMXpiPSzkF2gZLUrNEpLgBQu2jBZFoYgsySiyl3Nz5eJYapYIa2FCvhCLuUV++5nfZq20xq7ULgJagHKzzNT6FP916Y95de4kj00+Rm+sl3uG7qE/8Y7R49tB52FI7IbcOc/RVlKvCnWrK564tv/hO9/u9xgpLchnuvfwl6tvc6mWJyZryKJIyTJxsDkR7+XIVhK3KqvbCMFG2RPTNvQGvncQsivHmyRIlBtlAP7RQ/+IiC/Cawuvsb66jiRIRPwR7hu9jy8c/QLdse4b7mOmnOHpi0+jWzqjnaNtYltpVnhj6Q1EUeTh3R/+z/p2EVADWI6FiLitWiQIAgIuoiAiCRJNs7mTsLuDHdxF7BCVa/D6wuv897P/nbAvTG/86jhsJBCh1Cjx0uxLfOX0V/ixe3/slts5vXia3/z2b3Jh/QJNo+ltwxfhgbEH+Nef/dckQjdpYZg1T4fh8+6fz81TaVQwHION8gaK7KMvGEOKpWgEemgYDS6svE1A0vjknk8CXhXk+MhxeuK9TC2+yF6thZQKostBpktF4v7DPDr+KHP5OTKlDHWjjiRIdEY6meyebDvYKpI3FSQKIoZl0B/vJxFIYDs2G9UNVFlFt3QEQaDQ8Fbnh/oOYdomwx3DBNQAuqVTqBd4ePxhfIqPp6eeZjG/yKH+Q0iiRLVVZT47D3iankK9gGmbLBYWydfyPLnvyTsnK7IKB34WTv2WR/ok1auwmHVQwzD+BS/M8COA+ztHiCk+XsnNM1fLY7guQ/4wxxID3JveddMpnCvGbzdLp9YtvW3SF/VF6Yx08j8+9j+ykFtgLjsHQF+8j6GOoVtWRKbWpyi3yoynt7veRvwRHNdhKjPF/r797df6qMNyLLoiXWxWNtmsbhL3x5El2atONgqko2kSwQQNo/HuG9vBDnZw29ghKtfg2xe+Ta1VY1/Pvuvuu5KK/J2L37klUZndmOXffPXfcHnzMr2xXmIdMVzXZbO2ydfe/hpNo8m//1v//sYGWoIIgsilzBRfn3qBpeISlmWi254A997h4/SGfIC3Kr7ifXFp4xIH+w+SDHmak2qrTGH1OTqql1ACQUQpzkZ2hp7yFP2pQ6jpcfb2euOpgiDQMltc3ryMbuvMZedIhpIkg0lmNmfQJI3RzlESwQSz2VnKjTIH+g9gWRb5eh6AZCBJrpZjqbjEJ6KfoCPcwXJhmYbRYLJ7koP9B7Esi9cWXiMZTLaFvplKhpbVal/IZrOzbFY2uW/XfcxuzvLm4pv0xnrvPIU2Ngz3/s+w9oqXBWS3vFDD7hPXjzN/yLE33sPeeA81U8d2HMKK9q6fx3DHMIlggrAWpqJXaOgNAloA13VpGA0vs0YJEA1E2ZW+Gp431DHEUMfQbe2XYRnM5+ZvmiEU9UfZqGyQKWc+NkRFlVV6Y70kg16OUqFRwHZsVEllID5AX6KPptH8WLW7drCDDwN2iMo12KxsIosygnhjEaBf9bNR2WhP1dwIXz71Zeayc4x1juG7RvnfHe1GlVVemXuFF2Ze4LHJx65/shphulbhr9/4r0zXmySCCTRfjExlnVKzxJnLz+Af+gRK79Uww6g/6o0HV7NtorK68hJu4RK++ChIKq7rsm7N4AR6iTeWqZSihFMH2ytyn+IjpIXY27sX27ZZLa0S8XvBe+vldZZLyzwz/QzVZpWOcAePjT9GX6yPleIKm9VNb7oIl6bRZLJ7EkVUSAQT7Onaw67OXWiKRstoYZqejgK8dlK5UW7nCoHXhjIdryXVHetmtbhKrpZ7T0Jm/AkY/QHv9jFA6A6cYUc7R9nXs4/V0irLxWXKjTJVvYokSliORcKfIOgL8sTeJ7ZVDu8Ejutgu3bb8fidEAQBQRBuWNH5qCIeiNOf6GdmY4YToycoN8vYthcpEfaFWcgv0BfrozP8Ho7XHexgBzfFDlG5BtFAtH2hvBF0UyfqjyLeRNFfa9U4OX8SVVa3kZQrSAQSrJfWeXb62RsTFUHgD6feINCssD/eS1UM4LgOmqKRVFQ0I89X12b5wX3KdV/clQtCrVlEL0whBdNe2wNP7GfbFoLiwxFVtPoyRmwM9RodjCRKdEW6ODxwmNnsLN++8G0UUfEuSI6NbuqUml5g3YszL3J48DD3DNzD3t697W1cylziib1PMNQxhCzJ2/r4PtVHV7SLqY0pumPdWI61jfBZ9pawdsuB9UrmkG7pN/0+dnBjJIIJntz3pHchdW18sg/TNDEcg7A/zGBikE8Mf4If3P+D7/k1NFkjGUyyWlolFohdd79u6p7Wxffx8V4RBIHDA4fJVDIs5BfojfV6x6mps5hfRJM1jgweufMK4A52sINbYoeoXIOHxh7iv5/57+Rr+XZ14goaegPLtnhk9yM3Hbu0HIu6Ub9p6VcQBCRRotKs3PD+86vn+W5mgb2+QVKCS69TxQbCkknVdrisdPJCqc6uzBT7er0WRsNooClae7RUbxVxjSpi7KorqiiKBHwhCtVNrEAcWc9jWo22YNd2vMC6oBbEdmyenX6WFy69wHJxmZbRombU0E0dwzYoNUuIZZHyxTISEkcGj6BbOpZt4eKiyMr2cetr8OD4g5xbPUe5UUaVVRRJwXK8AL9MxWsRXAnxaxiNtvncDu4cA8kBfu7Rn+PgwEFOzp2kUC+gyRq90V4meyc5MniEaCD67hu6CQRBYE/3HhZyC9RatW2TRY7rsFxcZiAxsK1i47oupUapPUF27XM+KuiN9/Lk3id5bf41Vkur3ki+KNMd7ebY0LHbbp3tYAc7uH3sEJVr8MjuR3hg7AGennqaptkkFUohCiKFRoGNygb7e/fzuUOfu+nzg2qQzkgnK4WVG95vWiaO69y03J6r5miZLXKRQV5VZDqcOpprUdZCXKiUqYl+mk6mTXQs22K5sMxE10Tbrl0SRQRB8gzdrmlPdUZ7yZczGFYTv3A1LwhgtbRKZ6STgcQAZ1fO8t2p71Kse9k/hXqBSqviJfDaNnWpjqp4ROzFyy9SbpYxHbPdxslWs/TGem9IVh4Zf4RTC6f49tS322LdaqWKKIiEfCEe3v0wIV/Ia1WV1hlPj9MR6sCyLbK1LJIg0RHq2Fmx3iaCWpDHJx/nUxOfaguVg1rwrjnMjqXH2KhscGrpFJvVTSL+CA2jwUpxhXggzmByEMdxkESJlcIKZ1bOsFxYxrRN/Kqfsc4xDg8c/sg53vYn+umJ9bBR2aBlttBkja5o103bwTvYwQ7eH3aIyjVQZIVffepXifgiPH/5ec+GHpegGuSR8Uf450/+czqjN+8/K7LCE3ue4K3Ftyg2itsMtFzXZa28RtQf5fE9j9/w+YlQAk3RqOk1/GqKZSnmPVdywdaolTLYtteGWcov0TSbDHUM8cDYA+2LdyTQhRpI0WhuIoevEqJEpJPujiEKq6do+mIotohVy5Ov5wlrYe7fdT9+1c+bi29SapSoG3Vy9Rz1Vt0bO5YUz+jNMijWipiWSb6eRxIlBhODOK5DxB/h+ZnnqbQqPDz+8LapFMfxpkDiIe8CNp+bp9go0jSb9MX7eGzPY+zu2k2lWWGjskE8EOdA7wH+8KU/5Jvnv8lSYQlJkNjTvYfPH/48n9n/mY+NodgHDVEUvVTkuwxJlHhg7AF6471MZaZ4e+VtlgvLaLJGhQp/+vqf8vWzX6cv3sdmddMbVfeFCapBDMvg5PxJNiobfHrfpz9yZEUSJXpiPX/Tu7GDHXxfYIeovAPxUJz/9fP/K7Obs5xaPoXt2Ix1jnkjtdK7r5h++NAP88rcKzw99TT5ep5kIInlWOSreSRZ4u/d8/c4NHDohs/d27OXye5JXpt/jWQgiSh55EMQBDqCHawV1xjuGOaeoXsI+oLsSu3yRoGvcQ0VFR8d6SNszH+DRj2L5k8iiZ4Ze3c4TjQ9SkbrJ6/XKTQKuLj4FT/TmWlqeo2l/JJXOdHr2FsJw1faL0EtiO3aXgWlWUaWZGotz430UP8hxtJj6JbO2eWz9Mf7Ge+6OrZ6fu08z156lpAW4nOHP4fjOFSaFRbzi2RrWQBWCitoitb+HP7TM/+Jr7/9dUTBSwd2HIdX5l7h9NJpVkur/MxDP/NevuId3EVIosSuzl24rsvc5hx7e/bSMlvM5+aptqrMtGb4f9/6fxER6Uv00RnyiP6VCZrZ7Czn185zYvSjMTK+gx3s4HuPHaJyAwiCwK70rm2jm7eLoC/Ir33+19rutpu1TSQkJnsn+eFDP8yPHvnRm7YuRFHkJ+/7SWZzs1zIXKAv1kfYF6Zu1FkprhDzx/jFJ3+RxyYe4+T8SU4tn+Lsyll2d+3mQO8B5K2gvVT3UVy7RW79dcziNBaeIVXAF2dg/49xIDrO1859nXwjT8QXQZEUpjemeWvpLVaKKzSMBqqiegGG1wiHBUFAEiRUSUUURaL+KOlImvvH7ifq9/QOATWAJElMZabaREU3dc4sn8Gv+NstKlESSYQSJEIJZjdn6Yp2tf1W4sE4X37ry3zr/LdIhVOkwlcDC9PRNPO5ef7o5T/iwbEHmeieuOPv6FrUHIsV08B2XTpkhfTOaOkdw7ItTi2dQhIlRFFkamMKv+KnP9GPUBSoNquEfCFaRgvD8qIXlovLvL36Nt2xbkzbZF/PPsL+6wMYd7CDHexgh6h8AAj5Q/zCp36Bf/jAP2Q+P48syrc0z3Ich0VLJ2tbhAaP8Is/9Kv86fO/y6XNS6yUVlAllV2du/gH9/0D+qJ9/Osv/2uWC8ueoRcOfsXPof5D/OOH/7FX4hdEOvsfJJGcJJO7yFpxiWytTNPyUV+Y4szqn7KUXyIdSaMlNYJakLAWRkCgUC/QslqIgogqq9SsGqZtgShhWjoI4G69x6AWZDQ12iYpVxD2hSk0Cls6GZHN6ibZWpbB5OAN339npJNSo9QmKQDfOvctTMfcRlLAI0uDiUHOr5/nm+e++Z6JiuU4vFAvccqoU7BMHCAkSoyrAT4VjBG/iSB4B9djs7pJppwhHU17hEWQiPqjnn9QdRNJlNBkDcMyOLd2jmQoSVgLIwoi2UqWtxbf4tsXvs0PHfyhmwqxd7CDHXz/YoeofIDwa34meyZv+ZgNy+Bb1Tzzlo6+lYcT6hjhS3/r/yCWn6Nc3iAWjHHf8H0sl5b5377+v1FoFJjonmgTn0qrwiuzrwDwLz/9L9uVlbxl8fz6EmulNQRBYHrjFIu5RcrNMoNxT1fy9MWncVyHVDjVDiys6TV6Y70Em0FqRpOKXkcQBFzApwSQ1QAgE9ACDCQHrntPhmUQ9oXbGhLLttqeKIqosLt79zb/FEVSsB0b2/FaTa7rspBfIKzeeIUtSRKSIN1UtHw7+FatwEvNCkFRpF/WEIGya/NGq0rRMfk70U5C34eJwe8Flm1hORamZVJqlNojybql0zJa+BQfhm3guA66pXuhl6oPTdbImBmCWpDpzDR7evawp3vP3/C72cEOdvBhw86Z+G8QFcviL8pZFq0W3bJKSNZwHIeSY3NSr3EkOcqPDh9vt4qevfQs65V1DvYd3NY+ivgi7Erv4vTyac6unvVGhk2dZ6efZaOywVh6jKn1KQzTYFfnLk4veWm5LbuFYRuYtonlWKTDaQQE5nPzFOoFOuIDNASRgKljmA0USSXoC+OIMpZr058cvq5KYjs2lVaFY0PHEASBi+sX+Xdf+3e8MPNCO1057o+zv28/h/oOYbkWDb1BMuSlNoNXNfErfrJO9qafneM6+DX/e/rcV80Wp/Q6cVEmcc0KPo5ISJCYM5qcbtZ5IPjex3e/nxDUvHTmml7DcZ32sem6LoqkIIuyFyWheP4r7RwpAZpmk55oDxF/hIvrF5nomtgRSe9gBzvYhp05z79BnNXrLFothmVfe/UuiiIJWaFLVrlgNJjfMjxzHIc3F94kHojfUOMS8UVomS2mM9MALBWWWC2uMpQcwrTMttusIikEtSClZonV4iqJYIKOUAeVZoWW3SIZSjLZM0ks1EFLEIgHksRCCQZT4/SmRoiGkox172bfrgcJxnoo1Atts7mG0WA2O0tfrI+x9Bjz2Xl+6c9+idfmXyPij5AMJukMdZKr5/ja21/jv73+35jKTPHG4hssF5c5vXy6XVV5YPwBGkajrWm4FpVmBUVSuHfk3vf0uV8yWtQcm/gNxkkVUcQnSJxr1e5om5ZlsVJYYaWwgmVZ72m/PqpIBBMMdwxTapbahAU8waymaoh4bcSG0WhrnAzLYLPijTSPdI4Q8oXaHis72MEOdnAtdioqdxkto8Vr86/x1uJblFol0pE0x4ePc6j/0HUE47xexyeIyDcgHmFRZsMymDWajKp+HMfBsq2bBtEBCAjYrnehz9fyuLjIkkzdqKNbOvFAHAGvWrFeXvfSXkXJCwjUqxiWQdNpMto5ih1IkhcllEaRbGmdul5FlVW6k0McGr2PZGqY1fwSzcIS+c3L4HoXpvHOce7bdR9hX5j/9N3/xMzGDHt79+K4DhvlDQqNAv//9u47Oq76TPj4997po6nqvbniLheMDZjm4DVkgUB4N7uUsGFJYMkbeA+bAG+STdsAG7LZk2UT0k4IZ0NClpA3LARiakIzGONuI7lIsnodSaPp5f7eP649RrGNbbAsWX4+nDnHc+/16JmfsObR7/6e5wdg1ax0DnVSnV9NQ3UDNYU1rN+3HrfdzcKqhVy16Cpe3PUiTb1N1BbU4nV6UUoRioZoH2pnRf0KLpl1yYf6HiWVgQ5oR1nU7NA0IsrIrbH5IIZh3j57ufFl2kJtAJQHyrlo1kVcOufS3G24qUzTNJbVLiMUDdE51ElvuDe3s7ZhGPjz/HjtXnZ170LTNUYTZjt/m8XGwuqFVAQq6A334rQ7pReJEOIwU/+n6Ck0HBvmBy//gHda30EphdPm5N3Wd3ml8RUuOesSbj735jEfXHFlYP+AaW4djcSBGQar1UpdYR1vt7xNZbDysGsPzjyUeM2qGk07sOoVsGjmmo5MNoPL7qLIV8Su7l25suOskUVHJ56Km4tVC2oY1K0UVy5inreQeDJCNpslz+Ujz+HBYXcRNbJ4vcUsq19OODJA1sjmqoB0XSeWiLFu+zrsVnOvIYfVQYmvhHAijE234XA5GIoOYbVYWVS9CMeB217bO7dzVtlZTCuexjev+CYPrHuAvb17c79pu+1uLph5AV/7+NdwOj5c11q3pmOgUIZxxGQlrgyqLLbjaiz3q7d/xe82/w5d03MVTfsH9/OT135Cx3AH/3DeP5wRDeoKPAVcNv8yyvxlPLP1GfaHzJbyc8rmMBQboj3UTpm/jIpgBQ6bg3QmTWV+JYurF6MwO9auqlwliYoQ4jCSqJxE/7X+v3hj3xvMKpk1pj14f6SfP2z7A2X+Mq5YdEXueKHFSlPmyFPdyjDIoAhYDq2hOH/m+by7/116w725D0Uwf6vf27eXmoIaltctB6DIW4Su67lupAWeArpHunHZzRLhgDtAMp1kcHSQeCaORbdg1a3MLJlJhb+CUKgNt9WJOy+foOfwZmERI0uBxUaR209J3qHGdvFsli1d77Gh5W3aRrvRDnTPzXPm4ba5sVvt+Jw+NE0jlU7htrtzGxUWegrpGOpgIDJAeaCcZfXLeOzmx3ip8SX29u3FqltZXL2Y5fXLP/SHfzgWJtK1k5H4KE0uP7Pya8ZsQpkwsqSVYp4z75ivtbNzJ3/Y/gfy8/Ip85fljufn5dMf6eeFnS+wuHoxS2uXfqhYTzcBd4CLZl/E0tql7Ondw/7B/eaWEhY7TpuTTW2b2NW1C7fdzcyqmbmEe1/fPsoCZcwqnTXB70AIMRlJonKSdIQ62NCygYpAxWF7mBR5ihiODvNK4yv81by/ylXrzHPk0ZiMEzEyh1WY9BlpArqVWQeaucWSMQzDoNRfyub2zbSH2in1l5LOphmKDlHuL+emc2/C5zYrLqqCVVTnV9My0EJ9YT11hXWEouZWAEopZpfOJpqMAuAyXMwrn8e8inn4XD4GI4MU2RzUegvoy6apsNjHtNyPGVnSKGrtztzxjFLsTsZ4o28v2/r2gsNL0VlrCPc1YUmMMhwdJmqJmrM3um4uqNQ4bPdkpdShxZaYmxlevuCj74CcyWR4etvTrNu5ju6RbkI2N3FvETvyq1k+8wJK/GWMGFmGjAxz7W4WHEeisqFlA+F4mOnFh/fbKfIU0TXUxVstb50xicpBXqeXxTWLWVyzeMzxtfPWsqFlA7u6dxFOhGkfasem26grquPcaececXNDIYSQROUkae5vZjg2zILKBUc8X+IvoXukm/ZQO9OKpwEwx5HHImeCTYlRRrQsfosFQ8FgNo1V07gkL0CJ1c7bzW/zq7d/xf7QfnPzP6XoCfcQT8eZVTKLi2ZfxIUzL8y9Lpjt/C+afRHP73yeDa0bCEVC9Ef6GYwM4nF4mFM2h3AibMZctYC5ZXPJqAxb2rfQG+6lrrCOZNdOUp5iWn3FeK0OrJpG/EAJ9SyHm4oDMyGGUmxLRNgRG6F5oJWgMgi4g/QUVPMeoMWH8Q60MBIbJkuWVCbFSGIEr8tLQ1VDLubhuFnaOh4fWE9uepLH33kct93N7NLZWK129qVi7OvawSvxERYvuopybxGrXH4u8ARwH0dpck+4B5f96JVHboebruGuk/k2Tms2q41zZ5zLgqoF9Iz0kDWyeJ1eyvxlZ8TtMSHEhyOJykmmUMe+6AC7rnOFt4BSq50tiVGGjQygUW93scTpYaHLy/b27Tz8p4cZTYwyvWg6LruLrJGle6Sb/tF+zp1+LjeuvPGIr+9xePA6vKDMXiU1+TXUF9WDgrqiOs6pP4d4Ok5LfwtdI120DLQwEhuh2FeMy+aiuWsXKbWd4pKZuGqXoawOqm0OKm0OSq2HZln6s2la0gn0eJhsfJgifyk6sKConp7B/YQcHtDAqumMJkYZjAzitDq5cN6FlPjNW1jJdJL+0X7Om3Ferkz5ZOkL97Fu5zp8Tt+Yvi8z7G5qAw42du6grKiWz53zaXwnsPjV4/B8YJVKIp3A5zi99rA5FbxOL16ndKEVQhwfSVROkvqiegLuAP2j/ZT6Sw873zvSS5m/jKr8qjHH7brOeXl+znZ5GTIyWIB83Zq7PbJu1zoGo4MsrFyY6y9h0S1UBisxlMGrjS+wdmYtRc4IqCxYS8FeT08kzdNbn+aVxlco8ZVQW1hLeaAcr9NLMpOkub+ZaCrK+TPOZ2nNUt7c9ybDsWGW1izF7z7UPySRTtDS08iMvHzOm3HeEd97b8ZsQa8rsyxX18wFkV6nj9WzLmB93172D7XR0b2LbDaLz+WjPFBOwBWgc6iTVDZFJpthfsV8FlcvPuLX+Cg2t22mP9LP/Ir5h52z6RYqXD72Nr+NddnfwgkkKotrFvNy48tEEpHDbvclUgkMwzjjbvsIIcTJJonKSVKZX8nZdWfz3I7n8Dg8hy2mjaViXDT7IuxWO4ZhsK1jG++0vkNvuBe/y8/S2qUsqVkyps1+KBpie+d2SnwlR2yCVRMI0NH3GvvaHqOodh5ggVQTXX3P8VJripea9mLTbaSyKXZ27qRtsI2FVQsp9hVT7C2mqaeJRVWLcFqddA13UeovHZOkgLkh4cFrF1YtPOJvwjEji03T0C02QMNQ2Vyy4nP5mVZQiyMepjwZJ9+Tz9r5a+kY6mD/4H4cNgfn1J3DzNKZVOXX4LSd/L12YqkYGtpRK0pcDhexRIx4Kj5mg8djWVa7jCU1S1i/bz2V+ZUU5hWi6zqDkUH2h/azpHrJh+718mFFEhG6R7rJGlnyHHmUB8qlkkYIcVqTROUkumHFDYTjYTbu34hSCrvVTjwVx2V38fGFH+eyeZeRyWT4+Zs/58VdLxJPx3HanCQzSV5ufJkV9Su47cLbcpuzJdIJMtnMEZMDjQwBSwf9eoK4UQy2WsBc57G3Zx0+lSDodBLIqzS7wbqD9Ef62d65nZXOlQTzguzr25fbY2coNjSmkuj9gnlB9vbtJRQNHTGWPN1CCkWJpxCfy8dQdJgCTwEA0WSU/sgQhY48ggVVNFQ35DYanFE8g/bIAKGSGfy3ZiMy2EHAYmGpy8dylxfPB/SMOREH9yJKZVJH3G9pND5KwB044U3x7FY7t190Ox67h3fa3jHXoyjz1sb508/nM+d95rCZlvGSNbJsbtvMxtaN7Onbw+DoYG6vpEvnXMo5084hPy//lMQihBAnkyQqJ1HAHeCuS+9i4/6NbGzdyEjcXO/x/oZvT21+ime2PkN5sJwiz6EN98KJMH/e/Wf8bj+fu+BzgFnmGnQHGYoOEXQHx3wtO2GM9AAZvOR7ArnjPSM9DCTtFLkMnFo41zVW0zQKPYV0DXXRN9pHRaACXdMxDIOOUAedQ50AVOVXjdkxGcxW6NqB/46k1OpgdypORrcws2QGWzu20RvuJeAO0BcbIZaK4Aj3UBkooyJQceh1nT62uAK8ExvF53BjR6cvm2J3Ks7WxCg3B8vwWz76JnVLa5ZSGaykdaA1t6PzQYl0guHYMFcsvOKom0Z+kGBekDsvvZOW/hb29u0FoLawlhklMz5y3Cfizb1v8sS7T9Ay0EL3SLdZlm7Po3O4k45QBy39LVzZcCWV+Yf34BFCiMlMEpUTYBgGfeE+0kba/A38CLMLTruT82acd8T1HIlUgleaXsHtcI9JUsBsgV/qL2X9vvVcuehKSv2luOwuzpt+Hr9865fEUjHc9kO3JSzE2D80SG3+DM4qrcsdD8VCOG0O3E4nxe4wbfEwhQf6oOiajsViYSQ+gsPmIJqM8ov1v6Cxu5F9/ftAwVllZ7GsbtmYPXxC0RABVyA3S/KXCixWpttdNCZjuH0lLKxaTOtgC73JCL3pOPZIH3ODldQX1uGwmZVCCnjH4WVUt1KYSVB94LWVUkSMLNuTUZ4KD3Jj8PD1PifK5/ZxzZJr+PnrP2d753bKA+U4LA6GYkP0jfbRUNXAx+Z87CN9jbqiOuqK6o594TjoHenl8XceZ1/fPjqGOjCUgaZpDIwOYNEtRJNRNu7fSNAT5OrFV+ca/QkhxOlAEpXj9MbeN3hi4xNsadtCIpOgIK+AC2ddyI0rbsxVrhxL+1A7XSNdlPvLj3i+xFfCjs4d7O3bm1uQu3b+Wpp6m3in9R28Ti8Bd4BEOkEitocZAQ9XLbgQ+/tmHQ72IrFo5gzI5rbNxFIxnDYnpb5SXDYXsVSMxq5GmgeaMQyD8kA5c8rm0NTTxNbOrfSM9PDXi/6amoIaoskog5HBD6zG0TWNeY488jSdlnSClCef+rwg09NxBrt30RcfZu5f7CI9qFnpsTqwJ2O5zQUNlSWSjDKaGCWi4MXYMLNiIRpKpn+o2Y73u+SsS3Db3fxxxx/Z07uHjJHB7/RzdcPVXNVwFfme0/e2yOt7X6d1oNXcGymbosBTgPVAefVoYpRIKkLLQAu7unexLLTslM/2CCHERyGJynF4dtuzfOeP36E/0k/AFSDPnkdvuJdH3nyE7Z3befCTDx5XsqKU4ljVy+9vdgbgd/v5P6v/D8/vep7X9rxGKBrCYXNw0Vkf42NVCepKxk7ll3hL6Ax18nxTM+v2DTMaz5I20kQSEbqGu9A1HZ/Lh6EMMkaGBRUL0HXd/C0cja6RLtpD7Ty3/Tkunn0xNouNJTVLWFKz5APjtmga0x1uauxORg+0/ffqhfTqFn7Xu4doMjom0QlZrCQBu5Eiz1FExsjQF+5jNDmKrlmwWSyEsfLU3tcZGGzh4tkXf2DPkuOxYtoKltctpz3UTiKdoMRfcto3GUtn0uzu3Y2maQzFh8hz5OWSFDC3HMjEM4wmR+kL9zESH5nAaIUQ4sRJonIMQ9EhfvzqjxmKDTG7bHbuQ6DQW8hoYpR397/LL978BXevvfuYr1UZrKTYV0xfuO+It436R/sJ5gWpLagdc9zn9vHJpZ/kioVXMJIYwWl14nU6YfRpSDeDbRpoZlwlvhLCsT7+0NSM3VXPvMoykpkkkUSEUDREKpNiIDKAjm6uRznQaEvXdCqCFQTcZiI2FBsiPy8fv9vP7t7d7O7dzbSiaTRUN1DoPbyl/kE2TSffcmiNS3mgnLkVc9m0fxOF3sLcgs5oYpS0xUGBy4fb7mYwGmIkEcbrzEPHQgKw61bK/GXs7NpJMC/IymkrjznGx6LrOjWFNce+8DSRzCRz65Cy2SwOi2PMeYtuIWNkCNqDDMeGD0uEhRBispNE5Rhe3f0qrQOtVAQrxvymCmZ1h9vh5k9Nf+Jzqz5HIC/wga/ldri5aNZF/OLNXxCKhsZUYRyc8Vi7YO1RFzzabXaKbO9b25J3IUSzkGkGZQFNx02C7piNuApg0+wMRgdBmYtp64vqqQhUsLltMxkjw4zSsbcANE3D4/Qws3Qmm/Zv4rW9rxGOhTEwZ1te4AVqCmq4ccWNLKpedFzjp+s65884H7fdzXtd77Gvbx9KKfI8RRQEqrE68jCUwWgijMvmQMcspY3rFvJUhjINwp5CGrsbWVi58KQ3gzvd2Sw2SrwlOG1ONE0jnU3jVM5cOXvWyOY6wNosNoq8Rcd4RSGEmFwkUTmGruEuskZ2zELW9/M7/YSiIQYiA8dMVAD+euFf0zXcxZ92/4mOUAcuuyu3a/GKaSu4fvn1xx+cJR88fw2ZNkh3HGj4VkxbrJEZpR78Ln/utd12Nz6XD4tuwaJbSGQSRJPRw259ZI0sbYNt7O3bSzgRZkn1EgJ5ATQ0YqkYLf0t/PTPP+X/fvz/UhGsOHJcf8FutbNi2goWVC5gIDKAUoqAO8hvk1HeiI0wmEqQzqbxODwoIKppGJpGfSqJA7Oaqi3URjgelkTlLzhsDmaVzWJT2yY6hzoZig+RjZk9VLJGlmgyit/lxzAMZpXOojq/+tgvKoQQk4gkKsfgtJsVEoZhHLFxViqTwm6x56pZjsVutXPbBbdxTt05vNXyFr2jvQRdQZbWLmV53fLc1ztuuhPsM83HAVarGy2ZOmqVjsvuwuv00j7Ujt/lz/32PRwbpj3UztaOrUQSESqDlbQPtZNRGdw2N6FYiJSR4vV9r/ODV37AP5z/D9QV1h2xGd2R5DnyxiQan7S7iWazbImPELHnkdSsKF3DpmB6Osb8dAQwx17XdNkP5ijmlM1hWe0y+qP92EZshGNhRuIjGMrAbrFT5CmipqCGqxdfLWMohDjtnJJEJZlMsnz5crZu3crmzZtZtGhR7ty2bdu4/fbbeeeddygqKuJ//+//zZe+9KVTEdZxWVG/Ar/bT2+497BbMlkjSyga4vwZ51PqO/4yWqvVyrL6ZSyrX3aywwVgcfVinnz3SWqMmsM+mCKJCE6rk48v/Dh/3v1ndnXtoiq/inQ2TWN3IwORAWy6jWJvMaX+UqLJKLs6d6Hrei4hs1ltbGzdSKmvlOX1yzm77uzjTlbeL2C18bn8CjbF8nh8uJuYilBkc1KdTVKRTeW6tvRH+inyFJHvPn0rc8ZTgaeAyxZchq7pvNT4Er22XrJGFqfdSb4rn+nF0/nYnI8xt3zuRIcqhBAn7JQkKl/60pcoLy9n69atY46Hw2EuvfRSVq9ezY9+9CO2b9/OZz7zGQKBAJ/97GdPRWjHNLNkJqvPWs2T7z4JQ2ZVjdViJZaK0THUQdAd5Jol12CzfvTGZCfLBTMv4M29b9LY08jMkplYD3R4jSaj7O7dzeKaxVx/zvXMLZ/L7zf/nr19e+kc7iSWjDGrdBbJTJK+0T40TcNpc9IR6kBpipklM7HoFtx2N16Hl0BegLdb3qbMXzZms78T4dR1Vnry8eWX8fyu58l355Ofl4+maSilCEVDJNNJFs5cOKnGeLIp8ZVww4obWDFtBY09jXQMdeC2u6nKr2JG8Qyq8qs+VDIphBATbdwTleeee47nn3+eJ598kueee27Muccee4xUKsXPf/5z7HY7c+fOZcuWLXzve9+bNImKruv808f+CcMweKnxJZp6m8zjmk51sJq/P/fvuXj2xRMcJZAaheQIoDGtoIpbVt2SK58++KFvtVhZXL2Y65dfz57ePVgtVq5quIq9vXt5dc+rlHhLWFSziHda36F7l7lfTCwVQ2kKHZ1kOplbU1NaUUrQHWRgdIDdvbs/dKJy0JyyOcSSMTa3b6apt8nsmqsM/E4/5884n9mls0/OOE1huq4zs3TmYd13hRDidDauiUpvby+33HILv//973G7D1+Mun79elatWoXdfqiZ15o1a/jXf/1XhoaGCAaDh/2dZDJJMpnMPQ+Hw+MT/Pt43V6+edU3+ZvOv2F983piqRilvlIunHXhcTd7GzfpGPRuhNBuSIVB08FVyPLC+cy48l94u/UdOoc6sVlszCiZgVW3mv1YYiF6RnroHu4mlo4Rjpvt9m1dNqqCVQTzgvSGe7HpNrOJHArDMBiIDOC2u3OJg8/loyfc85Hfhq7rnF1/NtNLptMx1EEincBpc+ZiEUIIcWYat0RFKcVNN93ErbfeytKlS2ltbT3smp6eHurqxrYdLykpyZ07UqJy//33841vfGNcYv4gmqYxr3Ie8yrnnfKvfVSZJLS9BIPvgbsYAvVgGBDvh7aXyK84l7Xz1+Yub+xu5I87/4jX4UXTNEbiI+Tn5eNTPgYjg4SiITLZDPFAnLNrz+bNfW/SH+knkohgs9joGe3B4/CwctrKXPVIJpvB7jx5Ox7n5+XL5nlCCCFyTrgE4J577kHTtA98NDY28tBDDzE6Osq99957UgO+9957GRkZyT3a29tP6uufVkb2QWg3MXcZEc1BOmuAxQaecnAVQN8WiIcAM6HY1rENu8WO2+6mdaAVj9NDviefYm8xxd5i+iP9FHgK6B7ppthXzFUNVzG/Yj66puN1ellYuZArF13J8vrluW624USY6cXTjzvkUCRE93A3yXTy2BcLIYQ4453wjMpdd93FTTfd9IHX1NfX8/LLL7N+/XocjrFlu0uXLuW6667j0UcfpbS0lN7e3jHnDz4vLT1yFY3D4TjsNc9Ug23r6enbR3ummaxh4LI5qcyvoqagGocjCLEmGG0HVz6D0UF6w72U+kvpGekhloqN2cm4rqiO0eQobaE2bLqNvX17WVS1iPmV8ynIK8Bld7GwciE+lw8wy7LbQm2UB8qPK1HZ0raF53c+z87unWSyGQo9hayauYo1c9bgcXnGbYyEEEKc3k44USkqKqKo6NjdLf/jP/6Df/mXf8k97+rqYs2aNfzmN79h+fLlAKxYsYIvf/nLpNNpbDazouOFF15g1qxZR7ztIw5p7m9mf/MbpNJRHD6za24sFWNH53aGo0Msql6EXdMha85cGIZBVmVzLdUPzn4d5LQ5KQ+UU1tQS9dwFyPxEZShWDVjFTUFNezs3Mnevr10j3QDZmv2ikAFq2auyiUvR/P6ntf58Z9/zEh8hFJfKR6Hh77RPh554xGaepq445I7yHNKIzchhBCHG7c1KtXVY6tAPB7zt+Zp06ZRWWn2I/m7v/s7vvGNb3DzzTdz9913s2PHDr7//e/z7//+7+MV1pSQyqR4q/ktfJqNmrwAcbv5Ie+wOvA6vHQMd1DkLaRON8BqNpDzuXz4HD5G4iPmTsTKXEd0MFmJJqP4HD7mls8lPy+fQk8hn1zyyVwju4pABQurFtIb7sVQBgFXgMpg5TFLhsPxML/a8CsS6QQLqxbmjufn5TOaGGX9vvXMq5jHFYuuGI+hEkIIcZqb0DaVfr+f559/npaWFpYsWcJdd93FP//zP0+a0uTJqmOog55wD/aiuegqjW6kc+esFituu5ue3l1k7V7wVgFmV9hZZbPoH+3H7/ST58hjOD4MQMbIMBwbpjxYjq7rZIwMi2sWj+m2q2kapf5SFlYtpKG6gbqiuuPqa/JO6zt0DHVQX1R/2LmDeyX9efefyWazH3FUhBBCTEWnrIV+bW3tEXduXbBgAa+99tqpCmNKiKViKKVIeSoZTQ7ije4nbfORtuahKYOgEcVIJ0gVzMflPHQLraGqgVAkRFNvEz6Xj9aBVvrD/Vh1KxX5Ffhdfpr7m5lXPo9pRdNOSqyDkUGUUuYszhH4XX5CkZA5o+P+4FtIQgghzjyy18/pJJGAlhbsr/8R9rxE1prPUEGQTHmQPE8KV3YQpWn0aQ7C+TOxlo5t0e92uPnY3I9RX1TPru5deJ1eBiIDOK1O8vPyybPnsaRmCQurFh41sThRTpsTZZg9WI60z0wyncRhcxz3XkmnXDYLXV0wMABKQX4+VFaCVf7pCCHEqSA/bU8XQ0Pwwgvw6quUd+6nOt2Ga3Q3xUkd5fWSnDuNkVVLiFeWsXOwm3PrLsBmOzzZcNqczK2Yy9yKuWSNLBoaQ7EhskYWn8uH03aCmyIew4KqBQTzgvSF+ygNjK3kyhpZBqIDXN1w9eRMVPr74f/9P9i5EwYHIZ0GtxtmzoSrr4ZZsyY6QiGEmPIkUTkdZLPw7LPw/PPQ24vHMDg35mAPKUat4InF8W7ZjaV/lOaLF1I2d8FxtZw/uBv00XZZPhlqC2pZNXMV/7P1f8iSpdRXikW3EElEaB5opipYxSWzLxm3r/+htbfD978P775rJomxGNhsEAxCZyc0N8Ntt8HixRMdqRBCTGmSqJwOXnkFnnrKvAUxNAQjI5QqRZ5No8ejEzPCDAY96H1dNOyupGrtyknVdv7GFTei6zqv7n6VHZ07ALDpNmaXzeaGc26gtqh2YgP8S+EwPPGEmaSEQmaSkpdn3vrp7YWCAujuNr8n5eVwlJ4/QgghPjpJVCa7lhZ48UUYHjY/QEdHIRpFVwo/kKd8RHwBtFAWi3KRF3GghaJQPtGBH+KwObj5vJtZO28tOzp3kMlmKPIW0VDVgHUyrvVoboZt22BkBJJJKCoCizn7hMsJgz2gJ6BFh6a3oeQKkJ2JhRBiXEzCTwmRoxTs2mUmJ6OjkMmY6yR0HVwuyGaxRqIErAduSaQNc9HnyMhER35E5YFyygOTKIM6mr17zVmURAKczkNJipEAbQRsSRjugbgGTb+DJS5wrwL95K7vEUIIMcF9VMQxRKPmegir1dxs0OOBVMpMVDTN/ADVdfM6TYNAwPxzLDbRkZ/e0mkzKbRazWQRQKUgOwwqAxYnZKxgDYDFC8mtEH/90LVCCCFOGklUJjOlzIW0iQQUFprPLRYzaVHKTE4Mw3xkMubaCasVfNKP5CMpLzfHMS/PTAwNA4w4kAHNYSYyDhvYrFBXA9YKSO2GbP9ERy6EEFOO3PqZzNxus29HNArV1ebsSV+fuVYlHDYTFU0zrysoMP9cXg5VVSfly/eO9NI10oXT6mRa8bST1ltl0ps+3eyVEg6ba1T6eiEvY45vdBQsGgQ9UFcKdWWgeyHTBZlesBZPdPRCCDGlSKIymVkssGCB2T8lEoF588wZlrY2M3mJRs3f+n0+KC42b/2cd95HrkLpCHXw09d+yht73mAoPoTNYmNGyQyuXXItH1/w8SM2bptSysth7Vqzykopc+ZkYD9kDchzg9cNM6vgihVQ6Df/jqYBxoSGLYQQU5EkKpPdzJlwySVmuWx7u1mBEokcajxmt5sLaWfNMmdfVqwwZ14+pI5QB1/87RfZ2rEVn9NHkaeIVDbFtvZt7Ondw1B0iBtX3jhm5+UpR9Ng9Wrz9s/TTx8Ydx20GOQVwpxquOpcc0YFzPUrSgfdP7FxCyHEFCSJymRnscC115oJycsvm0lKXZ25YDYWA6/XvFUxbRosW2ae+wgee/sxtnVso66wDo/Dkzte4Cmgub+ZX771Sy6efTFVBSfn9tKkpWlw0UWwcKFZBdS5BdLvQvU0qKsH94FOusqATBvYKsyHEEKIk0oSldOB1Qqf+ITZBXX3brPpmK6bMyilpeYsS1nZR95/Zjg6zGu7X8Ntd49JUgB0Tac8UE7rQCuvNL7Cjefe+JG+1mlheNgsCy8thdl/A5Y5kHwX6ICMD0iDMQLWcrM8WTv2btJCCCFOjCQqpwtdN2dL6uoOVfycZKFYiOHEMB6n54jn3XY3Sim6R7pP+teeVCIR2LABtm+H1lZzzx+nE6ZPg0vmQa0C+zDgBOcSsE8HS2BiYxZCiClKEpXT0TitD3HZXTitTqLJ6BHPp7NpFAqv0zsuX39SiMfNdSmvvmre8gmFzOMul9kleH8brFkDq682+9oIIYQYV5KoiJwSbwmLqxfz7PZnSWfT2Cxjb2X0jvQScAe4aPZFExThKbB1Kzz3nLmAtrPT3IjQMMxSZbvd7FdzsGx81aqJjlYIIaa8KV5nKk6Eruv8r2X/i7JAGbt7djMUHSJrZEmmk7QOtjKaGGXNnDXMLJ050aGOn2efPbQNgdVqlnwXFh7a76e/35xZ2bLFXL8ihBBiXEmiIsZYWruUr17+VeZXzGcwMsh73e+xr38fbpubT539Kf5pzT9h0S0THeb4CIXMHjVWq9mjxu8/VOptsZjrVJQyz+3fL4mKEEKcAnLrRxxm1axVLKldwtvNb9M+1I7D6mBF/QpqCmsmOrTxFYmYiUkiYT7/y7VAVqvZcM9qNSuCLFM0YRNCiElEEhVxRHmOPC4+6+KJDuPUsljMMu+ODjMZSSbN9SgHJRLmOhVdN7sBFxRMXKxCCHGGkFs/QhxUXAyzZ5trUjTtUFO9dPrQrtR+v3lu2bKP3LdGCCHEsclPWiEOstlgyRKz4icSMWdW4nHzz5oGDoeZzMybBxefYbNNQggxQSRREeL9zjoLUinzFs9bb5kLbA3D3PyxpMTcJPLSSz/yxo9CCCGOjyQqQryfpsGiReb+SatWwb59ZklyMAj19ebxkpKJjlIIIc4YkqgIcSQeD5x9tvkQQggxYWQxrRBCCCEmLUlUhBBCCDFpSaIihBBCiElLEhUhhBBCTFqSqAghhBBi0pJERQghhBCTliQqQgghhJi0JFERQgghxKQliYoQQgghJi3pTHsCwvEw6Wwat92Ny+6a6HCEEEKIKU8SlQ+QyqRo7G6kfaidvtE+0pk0aSONy+ZiVuksFlQuwOv0TnSYQgghxJQlicpRvPzeyzyz7RkauxvpGulC0zSmFU6jobqBsB6mqaeJ5v5mPtHwCTxOz0SHK4QQQkxJkqgcwbod6/jZaz8jlU2haRqFnkIy2Qyb2zezs2sn8yrmkWfPY1//PmLpGH+/8u+xW+0THbYQQggx5chi2r/QPdzNT179Ce2hdkYTo+zp20P3SDf7B/cTS8Xoi/TR3N+Mz+XD6/Ty0q6XeOW9VzAMY6JDF0IIIaYcmVF5n3A8zM9e+xmN3Y1UBisZig8xFB3CUAaGMshz5GGkDfb07SFjZKguqMZusbOlYwsLqhZQFiib6LcghBBCTCmSqLzPpv2baB1sxWV3kcqm6B7uJplJYhgGyWySeCqO0+ZE13RiqRg9Iz247C46hzrpGOqQRGUSyRpZhqJD9IR7CMfDWC1WSn2lVAQrsOiWiQ5PCCHEcZJE5YBwPMyevj2U+8t5a99bDMeHMZSBzWIjkomglCJDhkQ6gVW3ks6kiSVjVAQr6B3tZTA6ONFvQQCRRISmniZe2/Ma2zq2kUgnKPAUYNEtZLNZyoPlrJm7hjnlc3DanBMdrhBCiGOQROWAaDJKNBWlPFiOruukk2k0TQMNUKChoSkNAwOH1UE8E8dqsVIVrKJ5oJloMjrRb+GMF46HWbdzHe+0vEPrQCtWqxWLbmFL+xYsuoVyfzn7Q/vZ27eXS866hNVnrabYVzzRYQshhPgAkqgcYLVYsek2hmPDVAQrSKQS9Iz2YLfYsVlsJDIJDGVg1a3ouo7T5sRusxNPxXHb3ATcgYl+C2e8dTvW8dzO5whFQnQOdWK32hmIDJgJJ5DJZijyFtEz3MP6fesxlME1i6+R5n1CCDGJSaJyQEFeAWX+Mvb07aEyWInX4eVPTX8iY2RAA4tuwaJZ8Dg9uc60uqYTSUaoyq+i0FM40W/hjLaraxdPb32adDbNaGKUZCZJf6SfSCKCw+7AMAxC0RDheBibxYbFYiGajHJW6VksqV0y0eELIYQ4inEtT/7DH/7A8uXLcblcBINBrrrqqjHn29rauPzyy3G73RQXF/PFL36RTCYzniEdla7rLKpehNvuJhQNURmsZHrxdIo8RQTdQfwuPzVFNThsDjJGBofFQaGnkNqCWmaUzKAyWDkhcQswDIM39r7BSGIEj8NDx1AHvZFe4qk4GZVhKDpEIp3AMAwGo4PE0jH8Lj8D0QFe3PUiSqmJfgtCCCGOYtxmVJ588kluueUW7rvvPi6++GIymQw7duzInc9ms1x++eWUlpby5ptv0t3dzY033ojNZuO+++4br7A+UH1RPZ9o+AQP/+lhOoc7cdqcOG1O3HY3iUyCVDqFw+pgetF0Sv2lZI0sXpeXhqoGCjwFExKzgJ5wD70jvWSMDLv7dpPMJNGURiqbIpqMYhgG6ay59YHNamMwMkjXSBfFnmI6hjvoG+2jxFcy0W9DCCHEEYxLopLJZLjjjjt48MEHufnmm3PH58yZk/vz888/z65du3jxxRcpKSlh0aJFfOtb3+Luu+/m61//Onb7xHR6XVa3DKUUz2x7hmBekN5wL4V5hURTUbqGu3DZXRTkFTAQGWBB5QIum38ZCyoXTEiswhRPxUkbaXMvpmwai24hlU2RyqZAgVW3YiiDrJElk83gtDmJJWOE9BAV+RUMRgYlURFCiElqXBKVTZs20dnZia7rNDQ00NPTw6JFi3jwwQeZN28eAOvXr2f+/PmUlBz6gFizZg233XYbO3fupKGh4YivnUwmSSaTuefhcPikx7+sbhnBvCBbO7ayoWUD+wf3Y9WtrKhfgdvuJqMyzCyeyccXflwW0U4CDquDSDJiznylE8RTcRxWB+lMGqUpsioLGmiaZpacW21ommYmLVan3PoRQohJbFwSlebmZgC+/vWv873vfY/a2lr+7d/+jQsvvJDdu3eTn59PT0/PmCQFyD3v6ek56mvff//9fOMb3xiPsHM0TWNGyQymFU3j4lkX0xfpY3B0kGgqisvuoia/RhqHTSIlvhLy7HlYdAulgVKa+5vJGlmzksuwmbMsmgWH1YHVYiWZTuJ3+XHanNgsNrltJ4QQk9gJLaa955570DTtAx+NjY25fW++/OUvc80117BkyRIeeeQRNE3jiSee+EgB33vvvYyMjOQe7e3tH+n1Poiu6+R78pldOptzZ5zLpXMv5fwZ51NdUC1JyiRis9pYVLUIpRSGYVAaKMXn8mG32vE4PPjdfrxOL3arHYfVQYmvhPrCejLZDDUFNXLbRwghJrETmlG56667uOmmmz7wmvr6erq7u4Gxa1IcDgf19fW0tbUBUFpayoYNG8b83d7e3ty5o3E4HDgcjhMJW5wBVk5fybaObezr34dFsxBwB4in4mT1LHaLnWgqitvuxu8yk5a+0T7qi+q5bP5luT4rQgghJp8TSlSKioooKio65nVLlizB4XDQ1NTEeeedB0A6naa1tZWamhoAVqxYwbe//W36+vooLja7g77wwgv4fL4xCY4Qx6PEV8LFZ12MzWojlU1h1a1ML55OY08j6Wya2qJaivOKGYgOUB4opzJYyZWLrpTOtEIIMcmNyxoVn8/Hrbfeyte+9jWqqqqoqanhwQcfBODaa68F4NJLL2XOnDnccMMNfOc736Gnp4evfOUr3H777TJjIk6YpmmsnLbSXKuiWdjYupGsyjKtcBopw0xcoukoFcEKltYu5ezas1lWt2yiwxZCCHEM49ZH5cEHH8RqtXLDDTcQj8dZvnw5L7/8MsFgEACLxcIzzzzDbbfdxooVK8jLy+PTn/403/zmN8crJDHFWS1WltQuYW7FXDa2bmRX1y4iiQhplSaZSlISKGFBxQJmlc4iPy9/osMVQghxHDR1mtdmhsNh/H4/IyMj+Hy+iQ5HTCIHO9Fmshm8Ti8ep2eiQxJCCHHA8X5+y14/YsrSdZ0i77HXVAkhhJi8xnWvHyGEEEKIj0ISFSGEEEJMWpKoCCGEEGLSkkRFCCGEEJOWJCpCCCGEmLQkURFCCCHEpCWJihBCCCEmLUlUhBBCCDFpSaIihBBCiEnrtO9Me3AHgHA4PMGRCCGEEOJ4HfzcPtZOPqd9ojI6OgpAVVXVBEcihBBCiBM1OjqK3+8/6vnTflNCwzDo6urC6/WiaRrhcJiqqira29vP+E0KZSwOkbE4RMbiEBmLQ2QsDpGxOGQ8x0IpxejoKOXl5ej60VeinPYzKrquU1lZedhxn893xv8PdpCMxSEyFofIWBwiY3GIjMUhMhaHjNdYfNBMykGymFYIIYQQk5YkKkIIIYSYtKZcouJwOPja176Gw+GY6FAmnIzFITIWh8hYHCJjcYiMxSEyFodMhrE47RfTCiGEEGLqmnIzKkIIIYSYOiRREUIIIcSkJYmKEEIIISYtSVSEEEIIMWlJoiKEEEKISWvKJSp/+MMfWL58OS6Xi2AwyFVXXTXmfFtbG5dffjlut5vi4mK++MUvkslkJibYUyCZTLJo0SI0TWPLli1jzm3bto3zzz8fp9NJVVUV3/nOdyYmyHHU2trKzTffTF1dHS6Xi2nTpvG1r32NVCo15rozYSwAfvCDH1BbW4vT6WT58uVs2LBhokMad/fffz/Lli3D6/VSXFzMVVddRVNT05hrEokEt99+OwUFBXg8Hq655hp6e3snKOJT54EHHkDTNO68887csTNpLDo7O7n++uspKCjA5XIxf/58Nm7cmDuvlOKf//mfKSsrw+VysXr1avbs2TOBEY+PbDbLV7/61TE/J7/1rW+N2SxwQsdCTSG//e1vVTAYVA8//LBqampSO3fuVL/5zW9y5zOZjJo3b55avXq12rx5s3r22WdVYWGhuvfeeycw6vH1hS98Qa1du1YBavPmzbnjIyMjqqSkRF133XVqx44d6te//rVyuVzqxz/+8cQFOw6ee+45ddNNN6l169apffv2qaeeekoVFxeru+66K3fNmTIWjz/+uLLb7ernP/+52rlzp7rllltUIBBQvb29Ex3auFqzZo165JFH1I4dO9SWLVvUZZddpqqrq1UkEsldc+utt6qqqir10ksvqY0bN6pzzjlHrVy5cgKjHn8bNmxQtbW1asGCBeqOO+7IHT9TxiIUCqmamhp10003qbfffls1NzerdevWqb179+aueeCBB5Tf71e///3v1datW9UVV1yh6urqVDwen8DIT75vf/vbqqCgQD3zzDOqpaVFPfHEE8rj8ajvf//7uWsmciymTKKSTqdVRUWF+tnPfnbUa5599lml67rq6enJHXv44YeVz+dTyWTyVIR5Sj377LNq9uzZaufOnYclKj/84Q9VMBgc877vvvtuNWvWrAmI9NT6zne+o+rq6nLPz5SxOPvss9Xtt9+ee57NZlV5ebm6//77JzCqU6+vr08B6s9//rNSSqnh4WFls9nUE088kbvmvffeU4Bav379RIU5rkZHR9WMGTPUCy+8oC644IJconImjcXdd9+tzjvvvKOeNwxDlZaWqgcffDB3bHh4WDkcDvXrX//6VIR4ylx++eXqM5/5zJhjV199tbruuuuUUhM/FlPm1s+mTZvo7OxE13UaGhooKytj7dq17NixI3fN+vXrmT9/PiUlJblja9asIRwOs3PnzokIe9z09vZyyy238F//9V+43e7Dzq9fv55Vq1Zht9tzx9asWUNTUxNDQ0OnMtRTbmRkhPz8/NzzM2EsUqkU7777LqtXr84d03Wd1atXs379+gmM7NQbGRkByP0/8O6775JOp8eMzezZs6murp6yY3P77bdz+eWXj3nPcGaNxf/8z/+wdOlSrr32WoqLi2loaOCnP/1p7nxLSws9PT1jxsLv97N8+fIpNxYrV67kpZdeYvfu3QBs3bqV119/nbVr1wITPxZTJlFpbm4G4Otf/zpf+cpXeOaZZwgGg1x44YWEQiEAenp6xiQpQO55T0/PqQ14HCmluOmmm7j11ltZunTpEa85U8biL+3du5eHHnqIz33uc7ljZ8JYDAwMkM1mj/g+p8p7PB6GYXDnnXdy7rnnMm/ePMD8HtvtdgKBwJhrp+rYPP7442zatIn777//sHNn0lg0Nzfz8MMPM2PGDNatW8dtt93GF77wBR599FHg0L/9M+HfzD333MOnPvUpZs+ejc1mo6GhgTvvvJPrrrsOmPixmPSJyj333IOmaR/4aGxsxDAMAL785S9zzTXXsGTJEh555BE0TeOJJ56Y4HdxchzvWDz00EOMjo5y7733TnTI4+Z4x+L9Ojs7+au/+iuuvfZabrnllgmKXEyk22+/nR07dvD4449PdCgTor29nTvuuIPHHnsMp9M50eFMKMMwWLx4Mffddx8NDQ189rOf5ZZbbuFHP/rRRId2yv33f/83jz32GL/61a/YtGkTjz76KN/97ndzSdtEs050AMdy1113cdNNN33gNfX19XR3dwMwZ86c3HGHw0F9fT1tbW0AlJaWHlblcHA1e2lp6UmMenwc71i8/PLLrF+//rBNpJYuXcp1113Ho48+Smlp6WEr+afiWBzU1dXFRRddxMqVK/nJT34y5rrTfSyOR2FhIRaL5Yjvc6q8x2P5/Oc/zzPPPMOrr75KZWVl7nhpaSmpVIrh4eExMwlTcWzeffdd+vr6WLx4ce5YNpvl1Vdf5T//8z9Zt27dGTMWZWVlYz4vAM466yyefPJJ4NC//d7eXsrKynLX9Pb2smjRolMW56nwxS9+MTerAjB//nz279/P/fffz6c//emJH4txXwVzioyMjCiHwzFmMW0qlVLFxcW56o2Di2nfX+Xw4x//WPl8PpVIJE55zONl//79avv27bnHunXrFKB++9vfqvb2dqXUoQWkqVQq9/fuvffeKbeAVCmlOjo61IwZM9SnPvUplclkDjt/pozF2WefrT7/+c/nnmezWVVRUTHlF9MahqFuv/12VV5ernbv3n3Y+YMLSH/729/mjjU2Nk7JBaThcHjMz4bt27erpUuXquuvv15t3779jBqLv/3bvz1sMe2dd96pVqxYoZQ6tID0u9/9bu78wc+ZqbaYNj8/X/3whz8cc+y+++5TM2bMUEpN/FhMmURFKaXuuOMOVVFRodatW6caGxvVzTffrIqLi1UoFFJKHSpPvvTSS9WWLVvUH//4R1VUVDSly5OVUqqlpeWwqp/h4WFVUlKibrjhBrVjxw71+OOPK7fbPeVKcjs6OtT06dPVJZdcojo6OlR3d3fucdCZMhaPP/64cjgc6he/+IXatWuX+uxnP6sCgcCYKrip6LbbblN+v1/96U9/GvP9j8ViuWtuvfVWVV1drV5++WW1ceNGtWLFitwH1lT3/qofpc6csdiwYYOyWq3q29/+ttqzZ4967LHHlNvtVr/85S9z1zzwwAMqEAiop556Sm3btk1deeWVU7I8+dOf/rSqqKjIlSf/7ne/U4WFhepLX/pS7pqJHIsplaikUil11113qeLiYuX1etXq1avVjh07xlzT2tqq1q5dq1wulyosLFR33XWXSqfTExTxqXGkREUppbZu3arOO+885XA4VEVFhXrggQcmJsBx9MgjjyjgiI/3OxPGQimlHnroIVVdXa3sdrs6++yz1VtvvTXRIY27o33/H3nkkdw18Xhc/eM//qMKBoPK7XarT3ziE2OS2ansLxOVM2ksnn76aTVv3jzlcDjU7Nmz1U9+8pMx5w3DUF/96ldVSUmJcjgc6pJLLlFNTU0TFO34CYfD6o477lDV1dXK6XSq+vp69eUvf3lMy4aJHAtNqfe1nhNCCCGEmEQmfdWPEEIIIc5ckqgIIYQQYtKSREUIIYQQk5YkKkIIIYSYtCRREUIIIcSkJYmKEEIIISYtSVSEEEIIMWlJoiKEEEKISUsSFSGEEEJMWpKoCCGEEGLSkkRFCCGEEJPW/weFUuWYoxYiiwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个基于预定义颜色的颜色映射对象\n",
    "colormap = matplotlib.colors.ListedColormap(colors)\n",
    "# 使用 matplotlib 创建散点图，其中颜色由颜色映射对象和颜色索引共同决定，alpha 是点的透明度\n",
    "plt.scatter(x, y, c=color_indices, cmap=colormap, alpha=0.3)\n",
    "\n",
    "# 为图形添加标题\n",
    "plt.title(\"Amazon ratings visualized in language using t-SNE\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**t-SNE降维后，评论大致分为3个大类。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 使用 K-Means 聚类，然后使用 t-SNE 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:04:18.481475Z",
     "start_time": "2024-06-11T03:04:18.310844Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# 从 scikit-learn中导入 KMeans 类。KMeans 是一个实现 K-Means 聚类算法的类。\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# np.vstack 是一个将输入数据堆叠到一个数组的函数（在垂直方向）。\n",
    "# 这里它用于将所有的 ada_embedding 值堆叠成一个矩阵。\n",
    "# matrix = np.vstack(df.ada_embedding.values)\n",
    "\n",
    "# 定义要生成的聚类数。\n",
    "n_clusters = 4\n",
    "\n",
    "# 创建一个 KMeans 对象，用于进行 K-Means 聚类。\n",
    "# n_clusters 参数指定了要创建的聚类的数量；\n",
    "# init 参数指定了初始化方法（在这种情况下是 'k-means++'）；\n",
    "# random_state 参数为随机数生成器设定了种子值，用于生成初始聚类中心。\n",
    "# n_init=10 消除警告 'FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4'\n",
    "kmeans = KMeans(n_clusters = n_clusters, init='k-means++', random_state=42, n_init=10)\n",
    "\n",
    "# 使用 matrix（我们之前创建的矩阵）来训练 KMeans 模型。这将执行 K-Means 聚类算法。\n",
    "kmeans.fit(matrix)\n",
    "\n",
    "# kmeans.labels_ 属性包含每个输入数据点所属的聚类的索引。\n",
    "# 这里，我们创建一个新的 'Cluster' 列，在这个列中，每个数据点都被赋予其所属的聚类的标签。\n",
    "df_embedded['Cluster'] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:04:20.517306Z",
     "start_time": "2024-06-11T03:04:20.510305Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12     1\n",
       "13     1\n",
       "14     1\n",
       "15     3\n",
       "16     1\n",
       "      ..\n",
       "447    2\n",
       "436    2\n",
       "437    0\n",
       "438    2\n",
       "439    1\n",
       "Name: Cluster, Length: 1000, dtype: int32"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded['Cluster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:04:36.095646Z",
     "start_time": "2024-06-11T03:04:36.084872Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UserId</th>\n",
       "      <th>Score</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Text</th>\n",
       "      <th>combined</th>\n",
       "      <th>n_tokens</th>\n",
       "      <th>embedding</th>\n",
       "      <th>embedding_vec</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>B000K8T3OQ</td>\n",
       "      <td>AK43Y4WT6FFR3</td>\n",
       "      <td>1</td>\n",
       "      <td>Broken in a million pieces</td>\n",
       "      <td>Chips were broken into small pieces. Problem i...</td>\n",
       "      <td>Title: Broken in a million pieces; Content: Ch...</td>\n",
       "      <td>120</td>\n",
       "      <td>[-0.0005399271612986922, -0.004124758299440145...</td>\n",
       "      <td>[-0.0005399271612986922, -0.004124758299440145...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>B0051C0J6M</td>\n",
       "      <td>AWFA8N9IXELVH</td>\n",
       "      <td>1</td>\n",
       "      <td>Deceptive description</td>\n",
       "      <td>On Oct 9 I ordered from a different vendor the...</td>\n",
       "      <td>Title: Deceptive description; Content: On Oct ...</td>\n",
       "      <td>157</td>\n",
       "      <td>[0.0068963742814958096, 0.0167608093470335, -0...</td>\n",
       "      <td>[0.0068963742814958096, 0.0167608093470335, -0...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ProductId         UserId  Score                     Summary  \\\n",
       "12  B000K8T3OQ  AK43Y4WT6FFR3      1  Broken in a million pieces   \n",
       "13  B0051C0J6M  AWFA8N9IXELVH      1       Deceptive description   \n",
       "\n",
       "                                                 Text  \\\n",
       "12  Chips were broken into small pieces. Problem i...   \n",
       "13  On Oct 9 I ordered from a different vendor the...   \n",
       "\n",
       "                                             combined  n_tokens  \\\n",
       "12  Title: Broken in a million pieces; Content: Ch...       120   \n",
       "13  Title: Deceptive description; Content: On Oct ...       157   \n",
       "\n",
       "                                            embedding  \\\n",
       "12  [-0.0005399271612986922, -0.004124758299440145...   \n",
       "13  [0.0068963742814958096, 0.0167608093470335, -0...   \n",
       "\n",
       "                                        embedding_vec  Cluster  \n",
       "12  [-0.0005399271612986922, -0.004124758299440145...        1  \n",
       "13  [0.0068963742814958096, 0.0167608093470335, -0...        1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_embedded.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:05:12.504854Z",
     "start_time": "2024-06-11T03:05:09.474099Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 首先为每个聚类定义一个颜色。\n",
    "colors = [\"red\", \"green\", \"blue\", \"purple\"]\n",
    "\n",
    "# 然后，你可以使用 t-SNE 来降维数据。这里，我们只考虑 'embedding_vec' 列。\n",
    "tsne_model = TSNE(n_components=2, random_state=42)\n",
    "vis_data = tsne_model.fit_transform(matrix)\n",
    "\n",
    "# 现在，你可以从降维后的数据中获取 x 和 y 坐标。\n",
    "x = vis_data[:, 0]\n",
    "y = vis_data[:, 1]\n",
    "\n",
    "# 'Cluster' 列中的值将被用作颜色索引。\n",
    "color_indices = df_embedded['Cluster'].values\n",
    "\n",
    "# 创建一个基于预定义颜色的颜色映射对象\n",
    "colormap = matplotlib.colors.ListedColormap(colors)\n",
    "\n",
    "# 使用 matplotlib 创建散点图，其中颜色由颜色映射对象和颜色索引共同决定\n",
    "plt.scatter(x, y, c=color_indices, cmap=colormap)\n",
    "\n",
    "# 为图形添加标题\n",
    "plt.title(\"Clustering visualized in 2D using t-SNE\")\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**K-MEANS 聚类可视化效果，4类（官方介绍：一个专注于狗粮，一个专注于负面评论，两个专注于正面评论）。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 使用 Embedding 进行文本搜索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![cosine](images/cosine.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:07:04.180110Z",
     "start_time": "2024-06-11T03:07:04.170332Z"
    }
   },
   "outputs": [],
   "source": [
    "# cosine_similarity 函数计算两个嵌入向量之间的余弦相似度。\n",
    "def cosine_similarity(a, b):\n",
    "    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:07:06.195214Z",
     "start_time": "2024-06-11T03:07:06.181206Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_embedded[\"embedding_vec\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:08:20.688691Z",
     "start_time": "2024-06-11T03:08:20.676730Z"
    }
   },
   "outputs": [],
   "source": [
    "# 定义一个名为 search_reviews 的函数，\n",
    "# Pandas DataFrame 产品描述，数量，以及一个 pprint 标志（默认值为 True）。\n",
    "def search_reviews(df, product_description, n=3, pprint=True):\n",
    "    product_embedding = embedding_text(product_description)\n",
    "    \n",
    "    df[\"similarity\"] = df.embedding_vec.apply(lambda x: cosine_similarity(x, product_embedding))\n",
    "\n",
    "    results = (\n",
    "        df.sort_values(\"similarity\", ascending=False)\n",
    "        .head(n)\n",
    "        .combined.str.replace(\"Title: \", \"\")\n",
    "        .str.replace(\"; Content:\", \": \")\n",
    "    )\n",
    "    if pprint:\n",
    "        for r in results:\n",
    "            print(r[:200]) #取前200的内容\n",
    "            print()\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:08:25.165697Z",
     "start_time": "2024-06-11T03:08:24.073546Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Good Buy:  I liked the beans. They were vacuum sealed, plump and moist. Would recommend them for any use. I personally split and stuck them in some vodka to make vanilla extract. Yum!\n",
      "\n",
      "Jamaican Blue beans:  Excellent coffee bean for roasting. Our family just purchased another 5 pounds for more roasting. Plenty of flavor and mild on acidity when roasted to a dark brown bean and befor\n",
      "\n",
      "Delicious!:  I enjoy this white beans seasoning, it gives a rich flavor to the beans I just love it, my mother in law didn't know about this Zatarain's brand and now she is traying different seasoning\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 使用 'delicious beans' 作为产品描述和 3 作为数量，\n",
    "# 调用 search_reviews 函数来查找与给定产品描述最相似的前3条评论。\n",
    "# 其结果被存储在 res 变量中。\n",
    "res = search_reviews(df_embedded, 'delicious beans', n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:09:03.345641Z",
     "start_time": "2024-06-11T03:09:02.281589Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healthy Dog Food:  This is a very healthy dog food. Good for their digestion. Also good for small puppies. My dog eats her required amount at every feeding.\n",
      "\n",
      "Doggy snacks:  My dog loves these snacks. However they are made in China and as far as I am concerned, suspect!!!! I found an abundance of American made ,human grade chicken dog snacks. Just Google fo\n",
      "\n",
      "Dogs Love Them!:  My Maltese and Cavalier King Charles love these treats!  I feel good about feeding them a healthier treat.<br />Not made in China!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = search_reviews(df_embedded, 'dog food', n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:09:09.587999Z",
     "start_time": "2024-06-11T03:09:08.550078Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "God Awful:  As a dabbler who enjoys spanning the entire spectrum of taste, I am more than willing to try anything once.  Both as a food aficionado and a lover of bacon, I just had to pick this up.  On\n",
      "\n",
      "Disappointed:  The metal cover has severely disformed. And most of the cookies inside have been crushed into small pieces. Shopping experience is awful. I'll never buy it online again.\n",
      "\n",
      "Just Bad:  Watery and unpleasant.  Like Yoohoo mixed with dirty dish water.  I find it quite odd that Keurig would release a product like this.  I'm sure they can come up with a decent hot chocolate a\n",
      "\n",
      "Arrived in pieces:  Not pleased at all. When I opened the box, most of the rings were broken in pieces. A total waste of money.\n",
      "\n",
      "Awesome:  They arrived before the expected time and were of fantastic quality. Would recommend to any one looking for a awesome treat\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = search_reviews(df_embedded, 'awful', n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:10:49.008492Z",
     "start_time": "2024-06-11T03:10:47.974613Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Healthy Dog Food:  This is a very healthy dog food. Good for their digestion. Also good for small puppies. My dog eats her required amount at every feeding.\n",
      "\n",
      "Doggy snacks:  My dog loves these snacks. However they are made in China and as far as I am concerned, suspect!!!! I found an abundance of American made ,human grade chicken dog snacks. Just Google fo\n",
      "\n",
      "Dogs Love Them!:  My Maltese and Cavalier King Charles love these treats!  I feel good about feeding them a healthier treat.<br />Not made in China!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def search_reviews(df, product_description, n=3, pprint=True):\n",
    "    product_embedding = embedding_text(product_description)\n",
    "\n",
    "    df[\"similarity\"] = df.embedding_vec.apply(lambda x: cosine_similarity(x, product_embedding))\n",
    "\n",
    "    results = (\n",
    "        df.sort_values(\"similarity\", ascending=False)\n",
    "        .head(n)\n",
    "        .combined.str.replace(\"Title: \", \"\")\n",
    "        .str.replace(\"; Content:\", \": \")\n",
    "    )\n",
    "    if pprint:\n",
    "        for r in results:\n",
    "            print(r[:100])\n",
    "            print()\n",
    "            print()\n",
    "    return results\n",
    "\n",
    "res = search_reviews(df_embedded, 'dog food', n=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:12:12.767518Z",
     "start_time": "2024-06-11T03:12:11.708756Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outstanding product:  try it & we shared with the familys/all han thumbs up!!!!!!!!!!cut it with good lite olive oil /sherry or whatever you like/on meat/chicken & fish $ pork!!\n",
      "\n",
      "expectations met:  Is what it says it is would never think it came out of a can would not be suprised to see this product on major. store shelves on account of quality\n",
      "\n",
      "Product is good, but....:  I like the product and the price point, but for me, the flavor was very strong and overpowering.  Perhaps I should have figured this out from the &#34;fog chaser&#34; name?\n",
      "\n",
      "Product is good, but....:  I like the product and the price point, but for me, the flavor was very strong and overpowering.  Perhaps I should have figured this out from the &#34;fog chaser&#34; name?\n",
      "\n",
      "Good coffee makes for a great morning.:  I was very happy to find this deal.  Good price and excellent coffee. Gets my morning off to a good start.\n",
      "\n",
      "good deal:  it's okay, but I would prefer a larger nugget type product. This ends up powder at the bottom of the bag.<br />Overall, it's a good deal, especially since it's organic.  Add a little flavo\n",
      "\n",
      "won't kid you:  I won't kid you there is nothing better than a real hot dog.  I love hot dogs but because of health reasons my husband &I had to cut back.  We tried linketts and found they are an exce\n",
      "\n",
      "Very pleased:  Bought this as a gift for our daughter in law.  She was very pleased.  So that makes us very pleased.  Nice gift.\n",
      "\n",
      "Very good:  Wasnt sure about buying such a large quantity of something untried but it was great and we have went through it pretty quickly. Will buy again.\n",
      "\n",
      "pretty good but not the best:  being raised in germany, i always had the great marzipan. when i got this box it actually did taste pretty good but it was somewhat dry, though i think the toasting is t\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = search_reviews(df_embedded, 'good point', n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-11T03:20:23.299335Z",
     "start_time": "2024-06-11T03:20:22.224795Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "outstanding product:  try it & we shared with the familys/all han thumbs up!!!!!!!!!!cut it with good lite olive oil /sherry or whatever you like/on meat/chicken & fish $ pork!!\n",
      "\n",
      "expectations met:  Is what it says it is would never think it came out of a can would not be suprised to see this product on major. store shelves on account of quality\n",
      "\n",
      "Product is good, but....:  I like the product and the price point, but for me, the flavor was very strong and overpowering.  Perhaps I should have figured this out from the &#34;fog chaser&#34; name?\n",
      "\n",
      "Product is good, but....:  I like the product and the price point, but for me, the flavor was very strong and overpowering.  Perhaps I should have figured this out from the &#34;fog chaser&#34; name?\n",
      "\n",
      "Good coffee makes for a great morning.:  I was very happy to find this deal.  Good price and excellent coffee. Gets my morning off to a good start.\n",
      "\n",
      "good deal:  it's okay, but I would prefer a larger nugget type product. This ends up powder at the bottom of the bag.<br />Overall, it's a good deal, especially since it's organic.  Add a little flavo\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = search_reviews(df_embedded, 'bad view', n=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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